import pandas as pd
import os
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
import matplotlib as plt
import matplotlib.pyplot as plt
from statsmodels.tsa.arima.model import ARIMA
import yfinance as yf
new_directory = r'C:\Users\DELL\Downloads'
os.chdir(new_directory)
# Read the CSV file into a pandas DataFrame
data = pd.read_csv('20180101_20180401_bist30.csv', index_col='timestamp', parse_dates=True)
company_1_data = data[data['short_name'] == 'THYAO']
fig, ax =plt.subplots()
company_1_data.plot(ax=ax)
plt.show()
company_1_data= company_1_data['price']
(640, 2)
First, I compared 3 different methods RF, linear regression and ARIMA. I first tried it with data 1, i.e. the first excel file; and looked at THYAO data. I took the 80% as train and the rest as test.For ARIMA, I used the parameters of 30,1,1; which were evaluated in the following codes.
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import numpy as np
import matplotlib.pyplot as plt
data=company_1_data
target_column = data.values
# Create a DataFrame with lag features
data = pd.DataFrame({'price': target_column})
# Feature engineering: Adding lag features
for i in range(1, 11):
data[f'lag_{i}'] = data['price'].shift(i)
# Drop rows with NaN values due to lag features
data = data.dropna()
# Split the data into training and testing sets
train_size = int(len(data) * 0.8)
train_data, test_data = data.iloc[:train_size], data.iloc[train_size:]
# Separate features and target variable
X_train, y_train = train_data.drop('price', axis=1), train_data['price']
X_test, y_test = test_data.drop('price', axis=1), test_data['price']
# Create a Random Forest Regressor
rf_model = Pipeline([
('scaler', StandardScaler()), # Standardize the data
('random_forest', RandomForestRegressor(n_estimators=100, random_state=42))
])
# Train the model
rf_model.fit(X_train, y_train)
# Make predictions on the test set
y_pred = rf_model.predict(X_test)
# Evaluate the model
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f'Root Mean Squared Error: {rmse}')
# Calculate MWAPE
def calculate_mwape(actual, predicted):
return np.mean(np.abs(actual - predicted) / np.abs(actual))
mwape = calculate_mwape(y_test, y_pred)
print(f'Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')
# Plotting actual vs. predicted prices
plt.figure(figsize=(12, 6))
plt.plot(test_data.index, y_test, label='Actual Prices', color='blue')
plt.plot(test_data.index, y_pred, label='Predicted Prices', color='orange')
plt.title('Random Forest Time Series Forecasting')
plt.xlabel('Timestamp')
plt.ylabel('Price')
plt.legend()
plt.show()
Root Mean Squared Error: 0.15999423055074027 Mean Weighted Absolute Percentage Error (MWAPE): 0.006686265522168634
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
# Assuming you have a Series named 'series'
# Replace 'your_data.csv' with your actual file or data loading method
# Create a Linear Regression model
linear_model = LinearRegression()
# Train the model
linear_model.fit(X_train, y_train)
# Make predictions on the test set
y_pred = linear_model.predict(X_test)
# Evaluate the model
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f'Root Mean Squared Error: {rmse}')
# Calculate MWAPE
def calculate_mwape(actual, predicted):
return np.mean(np.abs(actual - predicted) / np.abs(actual))
mwape = calculate_mwape(y_test, y_pred)
print(f'Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')
# Plotting actual vs. predicted prices
plt.figure(figsize=(12, 6))
plt.plot(test_data.index, y_test, label='Actual Prices', color='blue')
plt.plot(test_data.index, y_pred, label='Predicted Prices', color='orange')
plt.title('Linear Regression Time Series Forecasting')
plt.xlabel('Timestamp')
plt.ylabel('Price')
plt.legend()
plt.show()
Root Mean Squared Error: 0.12079557962652267 Mean Weighted Absolute Percentage Error (MWAPE): 0.004740389442806533
#ARIMA
##stationary
company_1_stationary= company_1_data.diff().dropna()
print(company_1_stationary)
from statsmodels.tsa.stattools import adfuller
# Assuming 'data' is your time series data
result = adfuller(company_1_stationary)
print('ADF Statistic:', result[0])
print('p-value:', result[1])
print('Critical Values:', result[4])
#stationary check, it is stationary
timestamp
2018-01-02 10:00:00+03:00 0.22
2018-01-02 11:00:00+03:00 0.04
2018-01-02 12:00:00+03:00 0.00
2018-01-02 13:00:00+03:00 0.01
2018-01-02 14:00:00+03:00 -0.01
...
2018-03-30 14:00:00+03:00 0.16
2018-03-30 15:00:00+03:00 -0.12
2018-03-30 16:00:00+03:00 -0.15
2018-03-30 17:00:00+03:00 0.03
2018-03-30 18:00:00+03:00 -0.09
Name: price, Length: 639, dtype: float64
ADF Statistic: -16.13966323231865
p-value: 4.690615998630029e-29
Critical Values: {'1%': -3.44065745275905, '5%': -2.8660879520543534, '10%': -2.5691919933016076}
##model=ARIMA(company_1_stationary, order=(p,d,q))
#p (AR - AutoRegressive): It indicates how many past observations influence the current one.
#d (I - Integrated): the number of differenciating needed to make the data stationary. if the dtaa is already stationary, set it to 0.
#q (MA - Moving Average): It represents the size of the moving average window and indicates the number of lagged forecast errors in the prediction equation.
model=ARIMA(company_1_data, order=(30,1,1))
results= model.fit()
print(results.summary())
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 640
Model: ARIMA(30, 1, 1) Log Likelihood 451.757
Date: Wed, 17 Jan 2024 AIC -839.513
Time: 13:05:44 BIC -696.796
Sample: 0 HQIC -784.114
- 640
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.2192 2.745 -0.080 0.936 -5.599 5.161
ar.L2 0.0960 0.173 0.555 0.579 -0.243 0.435
ar.L3 -0.0230 0.220 -0.104 0.917 -0.455 0.409
ar.L4 -0.0125 0.125 -0.100 0.920 -0.258 0.233
ar.L5 0.0390 0.042 0.933 0.351 -0.043 0.121
ar.L6 0.0609 0.110 0.555 0.579 -0.154 0.276
ar.L7 0.0137 0.142 0.096 0.923 -0.266 0.293
ar.L8 -0.0055 0.043 -0.126 0.899 -0.090 0.079
ar.L9 -0.0148 0.049 -0.304 0.761 -0.110 0.081
ar.L10 0.0093 0.058 0.161 0.872 -0.104 0.122
ar.L11 -0.0124 0.054 -0.227 0.820 -0.119 0.094
ar.L12 -0.0696 0.060 -1.153 0.249 -0.188 0.049
ar.L13 -0.0507 0.198 -0.256 0.798 -0.439 0.338
ar.L14 0.0134 0.106 0.127 0.899 -0.194 0.221
ar.L15 0.0225 0.078 0.289 0.773 -0.130 0.176
ar.L16 0.0193 0.067 0.289 0.773 -0.112 0.151
ar.L17 0.0169 0.061 0.278 0.781 -0.102 0.136
ar.L18 -0.0983 0.057 -1.710 0.087 -0.211 0.014
ar.L19 0.0504 0.286 0.176 0.860 -0.511 0.612
ar.L20 -0.0092 0.226 -0.041 0.968 -0.452 0.433
ar.L21 -0.0941 0.095 -0.988 0.323 -0.281 0.093
ar.L22 -0.0420 0.240 -0.175 0.861 -0.513 0.429
ar.L23 0.0550 0.069 0.802 0.422 -0.079 0.190
ar.L24 -0.0169 0.174 -0.097 0.923 -0.358 0.324
ar.L25 -0.0233 0.101 -0.231 0.818 -0.221 0.175
ar.L26 -0.0123 0.063 -0.196 0.845 -0.135 0.111
ar.L27 0.0167 0.059 0.285 0.776 -0.098 0.132
ar.L28 -0.0054 0.066 -0.082 0.934 -0.134 0.123
ar.L29 0.1052 0.049 2.150 0.032 0.009 0.201
ar.L30 0.0455 0.292 0.156 0.876 -0.526 0.617
ma.L1 0.2791 2.751 0.101 0.919 -5.113 5.671
sigma2 0.0142 0.001 22.371 0.000 0.013 0.015
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 200.26
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 1.60 Skew: 0.12
Prob(H) (two-sided): 0.00 Kurtosis: 5.73
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
forecast=results.get_prediction(start=-128)
mean_forecast=forecast.predicted_mean
confidence_intervals=forecast.conf_int()
print(confidence_intervals)
lower_limits = confidence_intervals.loc[:,'lower price']
upper_limits = confidence_intervals.loc[:,'upper price']
lower price upper price timestamp 2018-03-14 11:00:00+03:00 17.924113 18.391432 2018-03-14 12:00:00+03:00 17.849370 18.316688 2018-03-14 13:00:00+03:00 18.122321 18.589640 2018-03-14 14:00:00+03:00 18.093302 18.560621 2018-03-14 15:00:00+03:00 18.319440 18.786759 ... ... ... 2018-03-30 14:00:00+03:00 19.021689 19.489007 2018-03-30 15:00:00+03:00 19.186758 19.654077 2018-03-30 16:00:00+03:00 19.096760 19.564079 2018-03-30 17:00:00+03:00 18.923420 19.390738 2018-03-30 18:00:00+03:00 18.923556 19.390875 [128 rows x 2 columns]
time= company_1_data.index
plt.figure()
plt.plot(time[512:640], mean_forecast.values, color='red', label='forecast')
plt.fill_between(time[512:640], lower_limits, upper_limits, color='pink')
plt.plot(time[512:640], company_1_data[512:640], label='Actual Data') # Replace with your actual data
plt.show
# Evaluate the model
rmse = np.sqrt(mean_squared_error(company_1_data[512:640], mean_forecast.values ))
print(f'Root Mean Squared Error: {rmse}')
# Calculate MWAPE
def calculate_mwape(actual, predicted):
return np.mean(np.abs(actual - predicted) / np.abs(actual))
mwape = calculate_mwape(company_1_data[512:640], mean_forecast.values )
print(f'Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')
Root Mean Squared Error: 0.11624825010362062 Mean Weighted Absolute Percentage Error (MWAPE): 0.00452682322171308
Then, i tried with another company, ARCLK and another time interval. Both the previous and this comparison gave the result of ARIMA method is working the best.
data16= pd.read_csv('20210927_20211226_bist30.csv', index_col='timestamp', parse_dates=True)
companydata = data16[data16['short_name'] == 'ARCLK']
companydata= companydata['price']
data = companydata
target_column = data.values
# Create a DataFrame with lag features
data = pd.DataFrame({'price': target_column})
# Feature engineering: Adding lag features
for i in range(1, 11):
data[f'lag_{i}'] = data['price'].shift(i)
# Drop rows with NaN values due to lag features
data = data.dropna()
# Split the data into training and testing sets
train_size = int(len(data) * 0.8)
train_data, test_data = data.iloc[:train_size], data.iloc[train_size:]
# Separate features and target variable
X_train, y_train = train_data.drop('price', axis=1), train_data['price']
X_test, y_test = test_data.drop('price', axis=1), test_data['price']
#RF
# Create a Random Forest Regressor
rf_model = Pipeline([
('scaler', StandardScaler()), # Standardize the data
('random_forest', RandomForestRegressor(n_estimators=100, random_state=42))
])
# Train the model
rf_model.fit(X_train, y_train)
# Make predictions on the test set
y_pred = rf_model.predict(X_test)
# Evaluate the model
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f'RF:Root Mean Squared Error: {rmse}')
# Calculate MWAPE
def calculate_mwape(actual, predicted):
return np.mean(np.abs(actual - predicted) / np.abs(actual))
mwape = calculate_mwape(y_test, y_pred)
print(f'RF:Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')
# Plotting actual vs. predicted prices
plt.figure(figsize=(12, 6))
plt.plot(test_data.index, y_test, label='Actual Prices', color='blue')
plt.plot(test_data.index, y_pred, label='Predicted Prices', color='orange')
plt.title('Random Forest Time Series Forecasting')
plt.xlabel('Timestamp')
plt.ylabel('Price')
plt.legend()
plt.show()
#Linear Regression
# Create a Linear Regression model
linear_model = LinearRegression()
# Train the model
linear_model.fit(X_train, y_train)
# Make predictions on the test set
y_pred = linear_model.predict(X_test)
# Evaluate the model
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f'LR:Root Mean Squared Error: {rmse}')
# Calculate MWAPE
def calculate_mwape(actual, predicted):
return np.mean(np.abs(actual - predicted) / np.abs(actual))
mwape = calculate_mwape(y_test, y_pred)
print(f'LR:Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')
# Plotting actual vs. predicted prices
plt.figure(figsize=(12, 6))
plt.plot(test_data.index, y_test, label='Actual Prices', color='blue')
plt.plot(test_data.index, y_pred, label='Predicted Prices', color='orange')
plt.title('Linear Regression Time Series Forecasting')
plt.xlabel('Timestamp')
plt.ylabel('Price')
plt.legend()
plt.show()
##model=ARIMA(company_1_stationary, order=(p,d,q))
#p (AR - AutoRegressive): It indicates how many past observations influence the current one.
#d (I - Integrated): the number of differenciating needed to make the data stationary. if the dtaa is already stationary, set it to 0.
#q (MA - Moving Average): It represents the size of the moving average window and indicates the number of lagged forecast errors in the prediction equation.
model=ARIMA(companydata, order=(30,1,1))
results= model.fit()
time= data.index
forecast=results.get_prediction(start=-128)
mean_forecast=forecast.predicted_mean
confidence_intervals=forecast.conf_int()
lower_limits = confidence_intervals.loc[:,'lower price']
upper_limits = confidence_intervals.loc[:,'upper price']
plt.figure()
plt.plot(time[496:640], mean_forecast.values, color='red', label='forecast')
plt.fill_between(time[496:640], lower_limits, upper_limits, color='pink')
plt.plot(time[496:640], companydata[506:640], label='Actual Data') # Replace with your actual data
plt.show
# Evaluate the model
rmse = np.sqrt(mean_squared_error(companydata[506:640], mean_forecast.values ))
print(f'ARIMA:Root Mean Squared Error: {rmse}')
# Calculate MWAPE
def calculate_mwape(actual, predicted):
return np.mean(np.abs(actual - predicted) / np.abs(actual))
mwape = calculate_mwape(companydata[506:640], mean_forecast.values )
print(f'ARIMA:Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')
RF:Root Mean Squared Error: 2.8936798721471537 RF:Mean Weighted Absolute Percentage Error (MWAPE): 0.041290602494520025
LR:Root Mean Squared Error: 1.0828321818721736 LR:Mean Weighted Absolute Percentage Error (MWAPE): 0.013146473734283818
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
ARIMA:Root Mean Squared Error: 0.9260238039211902 ARIMA:Mean Weighted Absolute Percentage Error (MWAPE): 0.01197928330313338
Following code is only for visualization purposes
import pandas as pd
import matplotlib.pyplot as plt
forecast = results.get_prediction(start=len(company_1_data), end=len(company_1_data) + 24)
mean_forecast = forecast.predicted_mean
confidence_intervals = forecast.conf_int()
lower_limits = confidence_intervals.loc[:, 'lower price']
upper_limits = confidence_intervals.loc[:, 'upper price']
# Create numerical index for actual data
num_indices = range(1, len(company_1_data) + 1)
company_1_data.index = num_indices # Reindexing with numerical indices
# Create numerical index for forecasted data (641 to 665)
num_new_indices = range(len(company_1_data) + 1, len(company_1_data) + len(mean_forecast) + 1)
plt.figure()
# Plotting the actual data with numerical indices
plt.plot(num_indices, company_1_data, label='Actual Data', color='blue')
# Plotting the forecasted values with new numerical indices
plt.plot(num_new_indices, mean_forecast.values, color='red', label='Forecast')
# Fill between the confidence intervals
plt.fill_between(num_new_indices, lower_limits, upper_limits, color='pink')
plt.xlabel('Index') # Update with appropriate label
plt.ylabel('Price') # Update with appropriate label
plt.title('Actual Data and Forecast')
plt.legend()
plt.show()
data1= pd.read_csv('20180101_20180401_bist30.csv', index_col='timestamp', parse_dates=True)
data2= pd.read_csv('20180402_20180701_bist30.csv', index_col='timestamp', parse_dates=True)
data3= pd.read_csv('20180702_20180930_bist30.csv', index_col='timestamp', parse_dates=True)
data4= pd.read_csv('20181001_20181230_bist30.csv', index_col='timestamp', parse_dates=True)
data5= pd.read_csv('20181231_20190331_bist30.csv', index_col='timestamp', parse_dates=True)
data6= pd.read_csv('20190401_20190630_bist30.csv', index_col='timestamp', parse_dates=True)
data7= pd.read_csv('20190701_20190929_bist30.csv', index_col='timestamp', parse_dates=True)
data8= pd.read_csv('20190930_20191229_bist30.csv', index_col='timestamp', parse_dates=True)
data9= pd.read_csv('20191230_20200329_bist30.csv', index_col='timestamp', parse_dates=True)
data10= pd.read_csv('20200330_20200628_bist30.csv', index_col='timestamp', parse_dates=True)
data11= pd.read_csv('20200629_20200927_bist30.csv', index_col='timestamp', parse_dates=True)
data12= pd.read_csv('20200928_20201227_bist30.csv', index_col='timestamp', parse_dates=True)
data13= pd.read_csv('20201228_20210328_bist30.csv', index_col='timestamp', parse_dates=True)
data14= pd.read_csv('20210329_20210627_bist30.csv', index_col='timestamp', parse_dates=True)
data15= pd.read_csv('20210628_20210926_bist30.csv', index_col='timestamp', parse_dates=True)
data16= pd.read_csv('20210927_20211226_bist30.csv', index_col='timestamp', parse_dates=True)
data17= pd.read_csv('20211227_20220327_bist30.csv', index_col='timestamp', parse_dates=True)
data18= pd.read_csv('20220328_20220626_bist30.csv', index_col='timestamp', parse_dates=True)
data19= pd.read_csv('20220627_20220925_bist30.csv', index_col='timestamp', parse_dates=True)
data20= pd.read_csv('20220926_20221225_bist30.csv', index_col='timestamp', parse_dates=True)
data21= pd.read_csv('20221226_20230326_bist30.csv', index_col='timestamp', parse_dates=True)
data22= pd.read_csv('20230327_20230625_bist30.csv', index_col='timestamp', parse_dates=True)
data23= pd.read_csv('20230626_20230924_bist30.csv', index_col='timestamp', parse_dates=True)
data24= pd.read_csv('20230925_20231224_bist30.csv', index_col='timestamp', parse_dates=True)
Here, I have uploaded all the data and did cross validation, i.e. i took different train and test data sets. Then i found that 30,1,1 parameters will work the best considering all cases.
#15,17,18,19, 20, 21, 22, 23, 24
traindata=pd.concat([data1, data2, data3,data4, data5, data6, data7, data8, data9, data10, data11,data12, data13, data14, data15,data16,data17,data18,data19,data20,data21,data22,data23])
testdata=data24
# Display combined_data
print(traindata.head(10))
company_1_data = traindata[traindata['short_name'] == 'THYAO']
company_1_data= company_1_data.drop(columns=['short_name'])
company_1_datat= testdata[testdata['short_name'] == 'THYAO']
company_1_datat= company_1_datat.drop(columns=['short_name'])
company_1_datatest= company_1_datat
price short_name timestamp 2018-01-02 09:00:00+03:00 15.79 THYAO 2018-01-02 10:00:00+03:00 16.01 THYAO 2018-01-02 11:00:00+03:00 16.05 THYAO 2018-01-02 12:00:00+03:00 16.05 THYAO 2018-01-02 13:00:00+03:00 16.06 THYAO 2018-01-02 14:00:00+03:00 16.05 THYAO 2018-01-02 15:00:00+03:00 16.11 THYAO 2018-01-02 16:00:00+03:00 16.13 THYAO 2018-01-02 17:00:00+03:00 16.08 THYAO 2018-01-02 18:00:00+03:00 16.08 THYAO
from statsmodels.tsa.stattools import adfuller
##stationary
company_1_stationary= company_1_data.diff().dropna()
print(company_1_stationary)
# Assuming 'data' is your time series data
resultxx = adfuller(company_1_stationary)
print('ADF Statistic:', resultxx[0])
print('p-value:', resultxx[1])
print('Critical Values:', resultxx[4])
#stationary check, it is stationary(ADF lower than critical values and p-value close to 0)
price
timestamp
2018-01-02 10:00:00+03:00 0.22
2018-01-02 11:00:00+03:00 0.04
2018-01-02 12:00:00+03:00 0.00
2018-01-02 13:00:00+03:00 0.01
2018-01-02 14:00:00+03:00 -0.01
... ...
2023-10-20 14:00:00+03:00 -0.20
2023-10-20 15:00:00+03:00 -0.20
2023-10-20 16:00:00+03:00 -1.50
2023-10-20 17:00:00+03:00 0.20
2023-10-20 18:00:00+03:00 -0.30
[14426 rows x 1 columns]
ADF Statistic: -16.616757692900425
p-value: 1.7119371132762855e-29
Critical Values: {'1%': -3.4308046727350603, '5%': -2.8617409450488616, '10%': -2.5668769583088533}
##model=ARIMA(company_1_stationary, order=(p,d,q))
#p (AR - AutoRegressive): It indicates how many past observations influence the current one.
#d (I - Integrated): the number of differenciating needed to make the data stationary. if the dtaa is already stationary, set it to 0.
#q (MA - Moving Average): It represents the size of the moving average window and indicates the number of lagged forecast errors in the prediction equation.
model=ARIMA(company_1_data, order=(30,1,1))
results= model.fit()
print(results.summary())
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 640
Model: ARIMA(30, 1, 1) Log Likelihood 451.757
Date: Wed, 17 Jan 2024 AIC -839.513
Time: 13:22:19 BIC -696.796
Sample: 0 HQIC -784.114
- 640
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.2192 2.745 -0.080 0.936 -5.599 5.161
ar.L2 0.0960 0.173 0.555 0.579 -0.243 0.435
ar.L3 -0.0230 0.220 -0.104 0.917 -0.455 0.409
ar.L4 -0.0125 0.125 -0.100 0.920 -0.258 0.233
ar.L5 0.0390 0.042 0.933 0.351 -0.043 0.121
ar.L6 0.0609 0.110 0.555 0.579 -0.154 0.276
ar.L7 0.0137 0.142 0.096 0.923 -0.266 0.293
ar.L8 -0.0055 0.043 -0.126 0.899 -0.090 0.079
ar.L9 -0.0148 0.049 -0.304 0.761 -0.110 0.081
ar.L10 0.0093 0.058 0.161 0.872 -0.104 0.122
ar.L11 -0.0124 0.054 -0.227 0.820 -0.119 0.094
ar.L12 -0.0696 0.060 -1.153 0.249 -0.188 0.049
ar.L13 -0.0507 0.198 -0.256 0.798 -0.439 0.338
ar.L14 0.0134 0.106 0.127 0.899 -0.194 0.221
ar.L15 0.0225 0.078 0.289 0.773 -0.130 0.176
ar.L16 0.0193 0.067 0.289 0.773 -0.112 0.151
ar.L17 0.0169 0.061 0.278 0.781 -0.102 0.136
ar.L18 -0.0983 0.057 -1.710 0.087 -0.211 0.014
ar.L19 0.0504 0.286 0.176 0.860 -0.511 0.612
ar.L20 -0.0092 0.226 -0.041 0.968 -0.452 0.433
ar.L21 -0.0941 0.095 -0.988 0.323 -0.281 0.093
ar.L22 -0.0420 0.240 -0.175 0.861 -0.513 0.429
ar.L23 0.0550 0.069 0.802 0.422 -0.079 0.190
ar.L24 -0.0169 0.174 -0.097 0.923 -0.358 0.324
ar.L25 -0.0233 0.101 -0.231 0.818 -0.221 0.175
ar.L26 -0.0123 0.063 -0.196 0.845 -0.135 0.111
ar.L27 0.0167 0.059 0.285 0.776 -0.098 0.132
ar.L28 -0.0054 0.066 -0.082 0.934 -0.134 0.123
ar.L29 0.1052 0.049 2.150 0.032 0.009 0.201
ar.L30 0.0455 0.292 0.156 0.876 -0.526 0.617
ma.L1 0.2791 2.751 0.101 0.919 -5.113 5.671
sigma2 0.0142 0.001 22.371 0.000 0.013 0.015
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 200.26
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 1.60 Skew: 0.12
Prob(H) (two-sided): 0.00 Kurtosis: 5.73
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
forecast = results.get_prediction(start=len(company_1_data), end=len(company_1_data) + 9)
mean_forecast = forecast.predicted_mean
confidence_intervals = forecast.conf_int()
lower_limits = confidence_intervals.loc[:, 'lower price']
upper_limits = confidence_intervals.loc[:, 'upper price']
print(mean_forecast)
print(confidence_intervals)
640 19.106957
641 19.084017
642 19.046349
643 19.003089
644 19.002918
645 19.018835
646 18.983433
647 18.955846
648 18.953437
649 18.959358
Name: predicted_mean, dtype: float64
lower price upper price
640 18.873297 19.340616
641 18.743530 19.424504
642 18.613653 19.479044
643 18.498936 19.507242
644 18.436056 19.569780
645 18.392345 19.645325
646 18.297282 19.669584
647 18.213774 19.697919
648 18.159284 19.747589
649 18.117652 19.801063
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
plt.figure()
# Plotting the forecasted values and confidence intervals
plt.plot(mean_forecast.values, color='red', label='Forecast')
plt.fill_between(range(len(mean_forecast)), lower_limits, upper_limits, color='pink', alpha=0.3)
# Plotting the first 25 points of company_1_data2 as 'Actual Data'
plt.plot(range(10), company_1_datatest.iloc[:10], label='Actual Data', marker='o') # Adjust the slicing as needed
plt.xlabel('Index') # Update with appropriate label
plt.ylabel('Values') # Update with appropriate label
plt.title('Comparison between Forecast and Actual Data')
plt.legend()
plt.show()
from sklearn.metrics import mean_squared_error
import numpy as np
# Assuming 'actual_data' contains your actual data and 'mean_forecast' contains the mean forecast
# Convert 'actual_data' and 'mean_forecast' to numpy arrays if they are not already
actual_values = np.array(company_1_datatest.iloc[:10])
forecast_values = np.array(mean_forecast)
# Calculate RMSE
rmse = np.sqrt(mean_squared_error(actual_values, forecast_values))
print(f"Root Mean Squared Error (RMSE): {rmse}")
#data4 - first 10
#20,1,10 : Root Mean Squared Error (RMSE): 0.21956257305143428
#30,1,10 :Root Mean Squared Error (RMSE): 0.2326478310491985
#2,1,1: Root Mean Squared Error (RMSE): 0.22378102154677745
#20,1,1: Root Mean Squared Error (RMSE): 0.2266429185264849
#20,1,20: Root Mean Squared Error (RMSE): 0.23786748201306226
#10,1,20:Root Mean Squared Error (RMSE): 0.24234514016904027
#10,1,10: Root Mean Squared Error (RMSE): 0.21992749712260598
#5,1,5 : Root Mean Squared Error (RMSE): 0.21384506077829027
#5,1,15 : Root Mean Squared Error (RMSE): 0.2193320424305745
#data 4- 100-110
#5,1,5 : Root Mean Squared Error (RMSE): 0.3823246691112294
#20,1,10 : Root Mean Squared Error (RMSE): 0.41061901519509086
#2,1,1: Root Mean Squared Error (RMSE): 0.3695443767607099
#2,1,4: Root Mean Squared Error (RMSE): 0.3599197914194394
#30,1,30:Root Mean Squared Error (RMSE): 0.4199183885303645
#data 4- 200-210
#2,1,4: Root Mean Squared Error (RMSE):0.6609514819410949
#20,1,10: Root Mean Squared Error (RMSE): 0.7099041139672777
#5,1,5: Root Mean Squared Error (RMSE): 0.6735294955328522
#2,1,1: Root Mean Squared Error (RMSE): 0.6438404448345101
#30,1,30: Root Mean Squared Error (RMSE): 0.6873118233406335
#50,1,50: Root Mean Squared Error (RMSE): 0.6808970326784374
#data 4 300-310
#2,1,1: Root Mean Squared Error (RMSE): 0.4962259971060228
#5,1,5: Root Mean Squared Error (RMSE): 0.49304198303767993
#2,1,4: Root Mean Squared Error (RMSE): 0.4990993617925751
#20,1,10: Root Mean Squared Error (RMSE): 0.4564365772463498
#30,1,30: Root Mean Squared Error (RMSE): 0.41448340116426996
#50,1,50: Root Mean Squared Error (RMSE): 0.4475610105688453
#data 4 400. data
#30,1,30: Root Mean Squared Error (RMSE): 0.058528847093128014
#2,1,1: Root Mean Squared Error (RMSE): 0.09914657230999979
#data 4 500.data
#2,1,1: Root Mean Squared Error (RMSE): 0.09015620713008844
#30,1,30: Root Mean Squared Error (RMSE): 0.10184539368529033
#data 9 first 10
#30,1,30: Root Mean Squared Error (RMSE): 0.3049194254793206
#2,1,1: Root Mean Squared Error (RMSE): 0.3002653180126832
#data 13 first 10
#2,1,1: Root Mean Squared Error (RMSE): 0.07527111565529547
#30,1,30: Root Mean Squared Error (RMSE): 0.08361974869901259
#data 20 first 10
#2,1,1 : Root Mean Squared Error (RMSE): 1.504191996762454
#30,1,30: Root Mean Squared Error (RMSE): 1.2950045320502124
#mean for comparison: 69.46, normalised (1.29)= 0.0185
#data 21 first 10
#2,1,1: Root Mean Squared Error (RMSE): 2.6380470886728817,,, Normalised=0.018
#30,1,30: Root Mean Squared Error (RMSE): 1.9196676855508452
#data 24 first 10
#2,1,1: Root Mean Squared Error (RMSE): 4.449751175853234
#30,1,30: Root Mean Squared Error (RMSE): 5.269123138874844
#5,1,5: RMSE: 4.5 ama trendi yakaladı
#30,1,1: Root Mean Squared Error (RMSE): 4.766827733192989
#30,1,5: Root Mean Squared Error (RMSE): 4.877291772333265
#data24 100.data
#5,1,5: Root Mean Squared Error (RMSE): 8.201136821838128, but predicts opposite trend
#2,1,1:Root Mean Squared Error (RMSE): 8.102811835768671
#30,1,1:Root Mean Squared Error (RMSE): 7.6783937147890615
#30,1,5: Root Mean Squared Error (RMSE): 7.951948663947562
#70,1,1: Root Mean Squared Error (RMSE): 8.923404637184442
#data24 200. data
#30,1,1:Root Mean Squared Error (RMSE): 5.245856233985591
#2,1,1: Root Mean Squared Error (RMSE): 5.155837010840061
#CHOOSING 30,1,1 by considering all
Root Mean Squared Error (RMSE): 4.766827733192989
Then, I put these codes into a function and applied to all companies to see how it worked. Then, I added yahoo finance data in a function (forecastplusyahoo) and tried all companies as well. I have adjusted some companies parameters, if the model is underpredicting I have adjusted to model to 30,1,3 parameters. You can search for "next" in the file to skip the results.
def arima_forecast(name, p, d, q):
# Filter train and test data for a specific short_name
company_2_data = traindata[traindata['short_name'] == name]
company_2_data = company_2_data.drop(columns=['short_name'])
print(company_2_data.tail(30))
company_2_datat = testdata[testdata['short_name'] == name]
company_2_datat = company_2_datat.drop(columns=['short_name'])
company_2_datatest = company_2_datat
mean_first_10 = company_2_datatest.head(10).mean()
print("Mean of the first 10 values:", mean_first_10)
print(company_2_datatest.head(10))
from statsmodels.tsa.stattools import adfuller
# Stationary check
company_2_stationary = company_2_data.diff().dropna()
print(company_2_stationary)
resultxx = adfuller(company_2_stationary)
print('ADF Statistic:', resultxx[0])
print('p-value:', resultxx[1])
print('Critical Values:', resultxx[4])
# ARIMA modeling
model = ARIMA(company_2_data, order=(p, d, q))
results = model.fit()
print(results.summary())
forecast = results.get_prediction(start=len(company_2_data), end=len(company_2_data) + 9)
mean_forecast = forecast.predicted_mean
confidence_intervals = forecast.conf_int()
lower_limits = confidence_intervals.loc[:, 'lower price']
upper_limits = confidence_intervals.loc[:, 'upper price']
print(mean_forecast)
print(confidence_intervals)
# Plotting the forecasted values and actual data
plt.figure()
plt.plot(mean_forecast.values, color='red', label='Forecast')
plt.fill_between(range(len(mean_forecast)), lower_limits, upper_limits, color='pink', alpha=0.3)
plt.plot(range(10), company_2_datatest.iloc[:10], label='Actual Data', marker='o')
plt.xlabel('Index')
plt.ylabel('Values')
plt.title('Comparison between Forecast and Actual Data')
plt.legend()
plt.show()
# Calculating RMSE
from sklearn.metrics import mean_squared_error
import numpy as np
actual_values = np.array(company_2_datatest.iloc[:10])
forecast_values = np.array(mean_forecast)
rmse = np.sqrt(mean_squared_error(actual_values, forecast_values))
print(f"Root Mean Squared Error (RMSE): {rmse}")
arima_forecast('THYAO', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 226.2
2023-09-20 10:00:00+03:00 225.4
2023-09-20 11:00:00+03:00 224.4
2023-09-20 12:00:00+03:00 224.1
2023-09-20 13:00:00+03:00 224.4
2023-09-20 14:00:00+03:00 225.4
2023-09-20 15:00:00+03:00 225.3
2023-09-20 16:00:00+03:00 224.8
2023-09-20 17:00:00+03:00 223.6
2023-09-20 18:00:00+03:00 223.3
2023-09-21 09:00:00+03:00 222.0
2023-09-21 10:00:00+03:00 218.9
2023-09-21 11:00:00+03:00 220.5
2023-09-21 12:00:00+03:00 221.3
2023-09-21 13:00:00+03:00 221.4
2023-09-21 14:00:00+03:00 227.9
2023-09-21 15:00:00+03:00 226.6
2023-09-21 16:00:00+03:00 228.6
2023-09-21 17:00:00+03:00 232.9
2023-09-21 18:00:00+03:00 232.6
2023-09-22 09:00:00+03:00 233.0
2023-09-22 10:00:00+03:00 231.1
2023-09-22 11:00:00+03:00 232.2
2023-09-22 12:00:00+03:00 231.7
2023-09-22 13:00:00+03:00 230.7
2023-09-22 14:00:00+03:00 228.8
2023-09-22 15:00:00+03:00 227.9
2023-09-22 16:00:00+03:00 228.2
2023-09-22 17:00:00+03:00 226.8
2023-09-22 18:00:00+03:00 226.1
Mean of the first 10 values: price 229.86
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 227.0
2023-09-25 10:00:00+03:00 226.9
2023-09-25 11:00:00+03:00 228.5
2023-09-25 12:00:00+03:00 229.7
2023-09-25 13:00:00+03:00 231.0
2023-09-25 14:00:00+03:00 229.9
2023-09-25 15:00:00+03:00 229.0
2023-09-25 16:00:00+03:00 231.3
2023-09-25 17:00:00+03:00 232.8
2023-09-25 18:00:00+03:00 232.5
price
timestamp
2018-01-02 10:00:00+03:00 0.22
2018-01-02 11:00:00+03:00 0.04
2018-01-02 12:00:00+03:00 0.00
2018-01-02 13:00:00+03:00 0.01
2018-01-02 14:00:00+03:00 -0.01
... ...
2023-09-22 14:00:00+03:00 -1.90
2023-09-22 15:00:00+03:00 -0.90
2023-09-22 16:00:00+03:00 0.30
2023-09-22 17:00:00+03:00 -1.40
2023-09-22 18:00:00+03:00 -0.70
[14226 rows x 1 columns]
ADF Statistic: -16.710760166698364
p-value: 1.4352632020639404e-29
Critical Values: {'1%': -3.43081108450881, '5%': -2.861743778546585, '10%': -2.5668784665510107}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood -12824.162
Date: Sun, 24 Dec 2023 AIC 25712.323
Time: 18:25:08 BIC 25954.334
Sample: 0 HQIC 25792.828
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.8750 0.014 -60.479 0.000 -0.903 -0.847
ar.L2 0.0401 0.004 10.080 0.000 0.032 0.048
ar.L3 0.0235 0.004 5.224 0.000 0.015 0.032
ar.L4 0.0255 0.004 5.782 0.000 0.017 0.034
ar.L5 0.0150 0.005 3.191 0.001 0.006 0.024
ar.L6 0.0575 0.005 12.036 0.000 0.048 0.067
ar.L7 0.0594 0.005 12.848 0.000 0.050 0.068
ar.L8 -0.0042 0.005 -0.851 0.395 -0.014 0.005
ar.L9 0.0021 0.005 0.426 0.670 -0.008 0.012
ar.L10 -0.0319 0.004 -8.095 0.000 -0.040 -0.024
ar.L11 -0.0525 0.004 -12.908 0.000 -0.060 -0.044
ar.L12 -0.0518 0.004 -11.713 0.000 -0.060 -0.043
ar.L13 -0.0392 0.005 -8.486 0.000 -0.048 -0.030
ar.L14 0.0402 0.005 8.474 0.000 0.031 0.050
ar.L15 0.0285 0.005 5.308 0.000 0.018 0.039
ar.L16 -0.0256 0.004 -5.847 0.000 -0.034 -0.017
ar.L17 0.0084 0.005 1.625 0.104 -0.002 0.019
ar.L18 0.0223 0.005 4.118 0.000 0.012 0.033
ar.L19 -0.0242 0.005 -4.644 0.000 -0.034 -0.014
ar.L20 -0.0406 0.004 -9.692 0.000 -0.049 -0.032
ar.L21 0.0040 0.005 0.825 0.409 -0.005 0.013
ar.L22 0.0137 0.005 2.676 0.007 0.004 0.024
ar.L23 -0.0288 0.005 -5.817 0.000 -0.039 -0.019
ar.L24 -0.0351 0.005 -7.376 0.000 -0.044 -0.026
ar.L25 0.0217 0.004 4.820 0.000 0.013 0.030
ar.L26 0.0515 0.005 10.527 0.000 0.042 0.061
ar.L27 0.0455 0.004 11.246 0.000 0.038 0.053
ar.L28 0.0267 0.005 5.122 0.000 0.016 0.037
ar.L29 0.0324 0.005 6.723 0.000 0.023 0.042
ar.L30 0.0464 0.003 13.977 0.000 0.040 0.053
ma.L1 0.9225 0.014 64.488 0.000 0.894 0.951
sigma2 0.3552 0.001 413.839 0.000 0.354 0.357
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 3634251.92
Prob(Q): 0.93 Prob(JB): 0.00
Heteroskedasticity (H): 73.28 Skew: 2.69
Prob(H) (two-sided): 0.00 Kurtosis: 81.12
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 225.829578
14228 225.733142
14229 225.891536
14230 225.850625
14231 225.722649
14232 225.596491
14233 225.677413
14234 225.423627
14235 225.195873
14236 225.212440
Name: predicted_mean, dtype: float64
lower price upper price
14227 224.661421 226.997735
14228 224.041448 227.424836
14229 223.804681 227.978392
14230 223.416471 228.284779
14231 222.983153 228.462144
14232 222.575362 228.617620
14233 222.377381 228.977444
14234 221.856409 228.990845
14235 221.387883 229.003863
14236 221.169361 229.255519
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 4.766827733192989
arima_forecast('AKBNK', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 29.06
2023-09-20 10:00:00+03:00 29.60
2023-09-20 11:00:00+03:00 29.84
2023-09-20 12:00:00+03:00 29.86
2023-09-20 13:00:00+03:00 29.94
2023-09-20 14:00:00+03:00 29.94
2023-09-20 15:00:00+03:00 30.00
2023-09-20 16:00:00+03:00 29.86
2023-09-20 17:00:00+03:00 29.56
2023-09-20 18:00:00+03:00 29.60
2023-09-21 09:00:00+03:00 29.94
2023-09-21 10:00:00+03:00 30.52
2023-09-21 11:00:00+03:00 30.50
2023-09-21 12:00:00+03:00 30.44
2023-09-21 13:00:00+03:00 30.68
2023-09-21 14:00:00+03:00 30.18
2023-09-21 15:00:00+03:00 30.16
2023-09-21 16:00:00+03:00 30.66
2023-09-21 17:00:00+03:00 31.24
2023-09-21 18:00:00+03:00 31.20
2023-09-22 09:00:00+03:00 31.20
2023-09-22 10:00:00+03:00 31.08
2023-09-22 11:00:00+03:00 31.26
2023-09-22 12:00:00+03:00 31.20
2023-09-22 13:00:00+03:00 31.44
2023-09-22 14:00:00+03:00 31.08
2023-09-22 15:00:00+03:00 30.90
2023-09-22 16:00:00+03:00 31.14
2023-09-22 17:00:00+03:00 30.84
2023-09-22 18:00:00+03:00 30.80
Mean of the first 10 values: price 31.734
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 30.94
2023-09-25 10:00:00+03:00 31.20
2023-09-25 11:00:00+03:00 31.32
2023-09-25 12:00:00+03:00 31.74
2023-09-25 13:00:00+03:00 31.98
2023-09-25 14:00:00+03:00 31.58
2023-09-25 15:00:00+03:00 31.78
2023-09-25 16:00:00+03:00 32.30
2023-09-25 17:00:00+03:00 32.28
2023-09-25 18:00:00+03:00 32.22
price
timestamp
2018-01-02 10:00:00+03:00 0.1127
2018-01-02 11:00:00+03:00 0.0352
2018-01-02 12:00:00+03:00 -0.0140
2018-01-02 13:00:00+03:00 0.0210
2018-01-02 14:00:00+03:00 0.0353
... ...
2023-09-22 14:00:00+03:00 -0.3600
2023-09-22 15:00:00+03:00 -0.1800
2023-09-22 16:00:00+03:00 0.2400
2023-09-22 17:00:00+03:00 -0.3000
2023-09-22 18:00:00+03:00 -0.0400
[14226 rows x 1 columns]
ADF Statistic: -18.260230314341218
p-value: 2.332033535288983e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 12257.874
Date: Sun, 24 Dec 2023 AIC -24451.748
Time: 18:25:47 BIC -24209.738
Sample: 0 HQIC -24371.243
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0046 0.290 0.016 0.987 -0.564 0.573
ar.L2 0.0226 0.005 4.747 0.000 0.013 0.032
ar.L3 0.0322 0.007 4.452 0.000 0.018 0.046
ar.L4 0.0038 0.010 0.373 0.709 -0.016 0.024
ar.L5 0.0309 0.004 7.737 0.000 0.023 0.039
ar.L6 0.0142 0.010 1.456 0.145 -0.005 0.033
ar.L7 -0.0021 0.005 -0.417 0.676 -0.012 0.008
ar.L8 -0.0079 0.005 -1.753 0.080 -0.017 0.001
ar.L9 0.0194 0.004 4.357 0.000 0.011 0.028
ar.L10 -0.0247 0.006 -3.930 0.000 -0.037 -0.012
ar.L11 -0.0094 0.008 -1.167 0.243 -0.025 0.006
ar.L12 -0.0144 0.005 -2.816 0.005 -0.024 -0.004
ar.L13 0.0020 0.006 0.339 0.734 -0.009 0.013
ar.L14 -0.0115 0.004 -3.037 0.002 -0.019 -0.004
ar.L15 0.0107 0.005 2.183 0.029 0.001 0.020
ar.L16 -0.0063 0.006 -1.126 0.260 -0.017 0.005
ar.L17 0.0333 0.005 7.401 0.000 0.024 0.042
ar.L18 0.0002 0.011 0.015 0.988 -0.021 0.021
ar.L19 -0.0043 0.004 -1.136 0.256 -0.012 0.003
ar.L20 -0.0042 0.004 -0.983 0.325 -0.013 0.004
ar.L21 0.0112 0.004 2.641 0.008 0.003 0.019
ar.L22 0.0141 0.005 2.586 0.010 0.003 0.025
ar.L23 0.0063 0.006 1.077 0.281 -0.005 0.018
ar.L24 -0.0071 0.004 -1.611 0.107 -0.016 0.002
ar.L25 -0.0298 0.004 -7.019 0.000 -0.038 -0.021
ar.L26 0.0101 0.010 1.068 0.286 -0.008 0.029
ar.L27 0.0172 0.005 3.194 0.001 0.007 0.028
ar.L28 -0.0074 0.007 -1.099 0.272 -0.021 0.006
ar.L29 -0.0020 0.004 -0.497 0.619 -0.010 0.006
ar.L30 0.0136 0.003 3.910 0.000 0.007 0.020
ma.L1 0.0036 0.290 0.012 0.990 -0.565 0.572
sigma2 0.0104 2.68e-05 389.963 0.000 0.010 0.011
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 2079922.52
Prob(Q): 0.97 Prob(JB): 0.00
Heteroskedasticity (H): 15.46 Skew: -0.44
Prob(H) (two-sided): 0.00 Kurtosis: 62.23
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 30.763473
14228 30.763770
14229 30.763265
14230 30.785909
14231 30.774089
14232 30.782824
14233 30.775300
14234 30.775289
14235 30.777172
14236 30.793909
Name: predicted_mean, dtype: float64
lower price upper price
14227 30.563124 30.963822
14228 30.479272 31.048268
14229 30.411714 31.114816
14230 30.374857 31.196961
14231 30.310688 31.237491
14232 30.269660 31.295989
14233 30.215530 31.335069
14234 30.172526 31.378052
14235 30.134648 31.419696
14236 30.112590 31.475227
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 1.0563051480457972
arima_forecast('ARCLK', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 154.5277
2023-09-20 10:00:00+03:00 155.6090
2023-09-20 11:00:00+03:00 154.9209
2023-09-20 12:00:00+03:00 154.4294
2023-09-20 13:00:00+03:00 153.9379
2023-09-20 14:00:00+03:00 153.5447
2023-09-20 15:00:00+03:00 152.3651
2023-09-20 16:00:00+03:00 152.0702
2023-09-20 17:00:00+03:00 151.4804
2023-09-20 18:00:00+03:00 151.4804
2023-09-21 09:00:00+03:00 151.4804
2023-09-21 10:00:00+03:00 149.0229
2023-09-21 11:00:00+03:00 149.4161
2023-09-21 12:00:00+03:00 148.9246
2023-09-21 13:00:00+03:00 150.0059
2023-09-21 14:00:00+03:00 153.9379
2023-09-21 15:00:00+03:00 153.0532
2023-09-21 16:00:00+03:00 153.7413
2023-09-21 17:00:00+03:00 155.5107
2023-09-21 18:00:00+03:00 155.6090
2023-09-22 09:00:00+03:00 155.7073
2023-09-22 10:00:00+03:00 154.8226
2023-09-22 11:00:00+03:00 155.3141
2023-09-22 12:00:00+03:00 155.3141
2023-09-22 13:00:00+03:00 155.9039
2023-09-22 14:00:00+03:00 155.2158
2023-09-22 15:00:00+03:00 155.0192
2023-09-22 16:00:00+03:00 155.5107
2023-09-22 17:00:00+03:00 153.3481
2023-09-22 18:00:00+03:00 154.0362
Mean of the first 10 values: price 156.59
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 155.6
2023-09-25 10:00:00+03:00 155.5
2023-09-25 11:00:00+03:00 156.3
2023-09-25 12:00:00+03:00 156.3
2023-09-25 13:00:00+03:00 157.6
2023-09-25 14:00:00+03:00 156.4
2023-09-25 15:00:00+03:00 156.5
2023-09-25 16:00:00+03:00 157.3
2023-09-25 17:00:00+03:00 157.2
2023-09-25 18:00:00+03:00 157.2
price
timestamp
2018-01-02 10:00:00+03:00 0.0853
2018-01-02 11:00:00+03:00 -0.1195
2018-01-02 12:00:00+03:00 -0.0171
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 0.1025
... ...
2023-09-22 14:00:00+03:00 -0.6881
2023-09-22 15:00:00+03:00 -0.1966
2023-09-22 16:00:00+03:00 0.4915
2023-09-22 17:00:00+03:00 -2.1626
2023-09-22 18:00:00+03:00 0.6881
[14226 rows x 1 columns]
ADF Statistic: -17.973416452398276
p-value: 2.7906236931361954e-30
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood -11274.773
Date: Sun, 24 Dec 2023 AIC 22613.545
Time: 18:26:10 BIC 22855.556
Sample: 0 HQIC 22694.050
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0029 0.184 0.016 0.987 -0.357 0.363
ar.L2 -0.0109 0.004 -2.787 0.005 -0.019 -0.003
ar.L3 0.0202 0.004 5.354 0.000 0.013 0.028
ar.L4 0.0090 0.005 1.754 0.079 -0.001 0.019
ar.L5 -0.0254 0.004 -6.803 0.000 -0.033 -0.018
ar.L6 -0.0063 0.006 -1.069 0.285 -0.018 0.005
ar.L7 0.0386 0.004 9.719 0.000 0.031 0.046
ar.L8 -0.0011 0.008 -0.139 0.890 -0.017 0.015
ar.L9 0.0147 0.003 4.304 0.000 0.008 0.021
ar.L10 0.0192 0.004 4.767 0.000 0.011 0.027
ar.L11 -0.0081 0.005 -1.636 0.102 -0.018 0.002
ar.L12 -0.0262 0.004 -6.660 0.000 -0.034 -0.018
ar.L13 -0.0221 0.006 -3.631 0.000 -0.034 -0.010
ar.L14 -0.0221 0.006 -3.948 0.000 -0.033 -0.011
ar.L15 0.0004 0.005 0.067 0.947 -0.010 0.011
ar.L16 0.0208 0.004 5.731 0.000 0.014 0.028
ar.L17 0.0117 0.005 2.185 0.029 0.001 0.022
ar.L18 0.0047 0.004 1.073 0.283 -0.004 0.013
ar.L19 0.0238 0.004 6.302 0.000 0.016 0.031
ar.L20 0.0167 0.005 3.100 0.002 0.006 0.027
ar.L21 -0.0079 0.005 -1.535 0.125 -0.018 0.002
ar.L22 0.0086 0.004 2.085 0.037 0.001 0.017
ar.L23 0.0074 0.004 1.768 0.077 -0.001 0.016
ar.L24 -0.0053 0.004 -1.354 0.176 -0.013 0.002
ar.L25 0.0009 0.004 0.224 0.823 -0.007 0.009
ar.L26 -0.0186 0.004 -4.849 0.000 -0.026 -0.011
ar.L27 -0.0298 0.005 -5.752 0.000 -0.040 -0.020
ar.L28 -0.0068 0.007 -1.033 0.302 -0.020 0.006
ar.L29 0.0057 0.004 1.457 0.145 -0.002 0.013
ar.L30 0.0216 0.004 6.164 0.000 0.015 0.028
ma.L1 0.0031 0.184 0.017 0.987 -0.357 0.363
sigma2 0.2857 0.001 316.033 0.000 0.284 0.287
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 528083.02
Prob(Q): 0.97 Prob(JB): 0.00
Heteroskedasticity (H): 81.27 Skew: 1.08
Prob(H) (two-sided): 0.00 Kurtosis: 32.77
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 154.047863
14228 154.066918
14229 154.064732
14230 154.185188
14231 154.363477
14232 154.340060
14233 154.311602
14234 154.424872
14235 154.491393
14236 154.507060
Name: predicted_mean, dtype: float64
lower price upper price
14227 153.000245 155.095480
14228 152.580948 155.552887
14229 152.249559 155.879904
14230 152.081378 156.288997
14231 152.001712 156.725241
14232 151.756914 156.923205
14233 151.527064 157.096140
14234 151.437841 157.411904
14235 151.315326 157.667460
14236 151.148154 157.865965
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 2.3726371950192835
arima_forecast('ASELS', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 39.60
2023-09-20 10:00:00+03:00 39.84
2023-09-20 11:00:00+03:00 39.34
2023-09-20 12:00:00+03:00 39.44
2023-09-20 13:00:00+03:00 39.32
2023-09-20 14:00:00+03:00 39.16
2023-09-20 15:00:00+03:00 38.96
2023-09-20 16:00:00+03:00 38.54
2023-09-20 17:00:00+03:00 38.08
2023-09-20 18:00:00+03:00 38.00
2023-09-21 09:00:00+03:00 37.86
2023-09-21 10:00:00+03:00 37.64
2023-09-21 11:00:00+03:00 37.70
2023-09-21 12:00:00+03:00 37.90
2023-09-21 13:00:00+03:00 37.98
2023-09-21 14:00:00+03:00 39.02
2023-09-21 15:00:00+03:00 38.98
2023-09-21 16:00:00+03:00 39.24
2023-09-21 17:00:00+03:00 39.68
2023-09-21 18:00:00+03:00 39.86
2023-09-22 09:00:00+03:00 39.86
2023-09-22 10:00:00+03:00 40.94
2023-09-22 11:00:00+03:00 40.78
2023-09-22 12:00:00+03:00 40.66
2023-09-22 13:00:00+03:00 40.52
2023-09-22 14:00:00+03:00 40.30
2023-09-22 15:00:00+03:00 40.16
2023-09-22 16:00:00+03:00 40.24
2023-09-22 17:00:00+03:00 40.64
2023-09-22 18:00:00+03:00 40.52
Mean of the first 10 values: price 41.686
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 40.62
2023-09-25 10:00:00+03:00 41.54
2023-09-25 11:00:00+03:00 41.82
2023-09-25 12:00:00+03:00 41.54
2023-09-25 13:00:00+03:00 41.88
2023-09-25 14:00:00+03:00 41.62
2023-09-25 15:00:00+03:00 41.56
2023-09-25 16:00:00+03:00 41.66
2023-09-25 17:00:00+03:00 42.32
2023-09-25 18:00:00+03:00 42.30
price
timestamp
2018-01-02 10:00:00+03:00 0.0144
2018-01-02 11:00:00+03:00 -0.0144
2018-01-02 12:00:00+03:00 0.0289
2018-01-02 13:00:00+03:00 0.0048
2018-01-02 14:00:00+03:00 -0.0096
... ...
2023-09-22 14:00:00+03:00 -0.2200
2023-09-22 15:00:00+03:00 -0.1400
2023-09-22 16:00:00+03:00 0.0800
2023-09-22 17:00:00+03:00 0.4000
2023-09-22 18:00:00+03:00 -0.1200
[14116 rows x 1 columns]
ADF Statistic: -17.371049345876518
p-value: 5.1374824316080754e-30
Critical Values: {'1%': -3.4308147547204983, '5%': -2.8617454004875986, '10%': -2.566879329894271}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14117
Model: ARIMA(30, 1, 1) Log Likelihood 7903.400
Date: Sun, 24 Dec 2023 AIC -15742.800
Time: 18:26:40 BIC -15501.038
Sample: 0 HQIC -15662.347
- 14117
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0078 2.065 0.004 0.997 -4.040 4.055
ar.L2 0.0058 0.032 0.181 0.856 -0.057 0.068
ar.L3 0.0110 0.012 0.908 0.364 -0.013 0.035
ar.L4 -0.0402 0.023 -1.779 0.075 -0.084 0.004
ar.L5 0.0035 0.083 0.042 0.967 -0.159 0.166
ar.L6 0.0362 0.008 4.287 0.000 0.020 0.053
ar.L7 0.0135 0.075 0.180 0.857 -0.133 0.160
ar.L8 -0.0188 0.027 -0.696 0.486 -0.072 0.034
ar.L9 0.0367 0.039 0.934 0.350 -0.040 0.114
ar.L10 -0.0412 0.076 -0.540 0.589 -0.191 0.108
ar.L11 -0.0257 0.086 -0.300 0.764 -0.194 0.142
ar.L12 -0.0430 0.053 -0.815 0.415 -0.146 0.060
ar.L13 -0.0064 0.088 -0.073 0.942 -0.179 0.167
ar.L14 -0.0175 0.013 -1.304 0.192 -0.044 0.009
ar.L15 0.0224 0.037 0.614 0.539 -0.049 0.094
ar.L16 0.0158 0.047 0.338 0.736 -0.076 0.107
ar.L17 0.0127 0.033 0.385 0.700 -0.052 0.077
ar.L18 0.0052 0.026 0.199 0.842 -0.046 0.056
ar.L19 0.0272 0.011 2.468 0.014 0.006 0.049
ar.L20 0.0195 0.056 0.346 0.729 -0.091 0.130
ar.L21 -0.0019 0.040 -0.048 0.962 -0.081 0.077
ar.L22 0.0241 0.006 4.152 0.000 0.013 0.035
ar.L23 -0.0084 0.050 -0.166 0.868 -0.107 0.090
ar.L24 -0.0384 0.018 -2.124 0.034 -0.074 -0.003
ar.L25 0.0078 0.079 0.099 0.921 -0.148 0.164
ar.L26 -0.0028 0.017 -0.160 0.873 -0.036 0.031
ar.L27 0.0141 0.007 1.900 0.057 -0.000 0.029
ar.L28 0.0096 0.029 0.326 0.745 -0.048 0.067
ar.L29 0.0144 0.020 0.720 0.471 -0.025 0.054
ar.L30 -0.0016 0.030 -0.055 0.956 -0.060 0.057
ma.L1 0.0076 2.065 0.004 0.997 -4.040 4.055
sigma2 0.0191 5.54e-05 345.000 0.000 0.019 0.019
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1001031.56
Prob(Q): 0.98 Prob(JB): 0.00
Heteroskedasticity (H): 32.31 Skew: 1.59
Prob(H) (two-sided): 0.00 Kurtosis: 44.13
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14117 40.556194
14118 40.507075
14119 40.486833
14120 40.467543
14121 40.526681
14122 40.550885
14123 40.586415
14124 40.657192
14125 40.645904
14126 40.607886
Name: predicted_mean, dtype: float64
lower price upper price
14117 40.285276 40.827112
14118 40.120978 40.893171
14119 40.011830 40.961835
14120 39.916309 41.018777
14121 39.913363 41.139999
14122 39.880923 41.220847
14123 39.860543 41.312286
14124 39.878103 41.436282
14125 39.818510 41.473299
14126 39.731610 41.484162
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 1.2079867998018134
arima_forecast('BIMAS', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 276.2
2023-09-20 10:00:00+03:00 275.6
2023-09-20 11:00:00+03:00 274.5
2023-09-20 12:00:00+03:00 272.0
2023-09-20 13:00:00+03:00 272.8
2023-09-20 14:00:00+03:00 271.8
2023-09-20 15:00:00+03:00 270.7
2023-09-20 16:00:00+03:00 270.8
2023-09-20 17:00:00+03:00 270.4
2023-09-20 18:00:00+03:00 270.0
2023-09-21 09:00:00+03:00 270.0
2023-09-21 10:00:00+03:00 270.4
2023-09-21 11:00:00+03:00 270.3
2023-09-21 12:00:00+03:00 270.2
2023-09-21 13:00:00+03:00 270.6
2023-09-21 14:00:00+03:00 276.0
2023-09-21 15:00:00+03:00 273.8
2023-09-21 16:00:00+03:00 274.3
2023-09-21 17:00:00+03:00 276.0
2023-09-21 18:00:00+03:00 275.7
2023-09-22 09:00:00+03:00 278.0
2023-09-22 10:00:00+03:00 274.1
2023-09-22 11:00:00+03:00 274.3
2023-09-22 12:00:00+03:00 276.2
2023-09-22 13:00:00+03:00 275.4
2023-09-22 14:00:00+03:00 274.0
2023-09-22 15:00:00+03:00 273.0
2023-09-22 16:00:00+03:00 272.4
2023-09-22 17:00:00+03:00 273.4
2023-09-22 18:00:00+03:00 273.4
Mean of the first 10 values: price 274.68
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 275.0
2023-09-25 10:00:00+03:00 272.1
2023-09-25 11:00:00+03:00 272.9
2023-09-25 12:00:00+03:00 273.9
2023-09-25 13:00:00+03:00 274.2
2023-09-25 14:00:00+03:00 273.9
2023-09-25 15:00:00+03:00 273.7
2023-09-25 16:00:00+03:00 274.8
2023-09-25 17:00:00+03:00 278.6
2023-09-25 18:00:00+03:00 277.7
price
timestamp
2018-01-02 10:00:00+03:00 0.3467
2018-01-02 11:00:00+03:00 -0.1226
2018-01-02 12:00:00+03:00 -0.1833
2018-01-02 13:00:00+03:00 0.1426
2018-01-02 14:00:00+03:00 0.1428
... ...
2023-09-22 14:00:00+03:00 -1.4000
2023-09-22 15:00:00+03:00 -1.0000
2023-09-22 16:00:00+03:00 -0.6000
2023-09-22 17:00:00+03:00 1.0000
2023-09-22 18:00:00+03:00 0.0000
[14226 rows x 1 columns]
ADF Statistic: -17.75951732138609
p-value: 3.3431484388348195e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood -15556.978
Date: Sun, 24 Dec 2023 AIC 31177.957
Time: 01:21:55 BIC 31419.967
Sample: 0 HQIC 31258.461
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.3678 0.258 1.425 0.154 -0.138 0.874
ar.L2 0.0170 0.014 1.256 0.209 -0.010 0.044
ar.L3 0.0003 0.004 0.083 0.934 -0.008 0.009
ar.L4 0.0167 0.004 4.369 0.000 0.009 0.024
ar.L5 0.0206 0.006 3.501 0.000 0.009 0.032
ar.L6 0.0375 0.008 4.661 0.000 0.022 0.053
ar.L7 0.0032 0.013 0.245 0.807 -0.023 0.029
ar.L8 0.0006 0.008 0.081 0.935 -0.015 0.016
ar.L9 0.0067 0.005 1.345 0.179 -0.003 0.016
ar.L10 0.0309 0.004 7.622 0.000 0.023 0.039
ar.L11 -0.0500 0.010 -5.173 0.000 -0.069 -0.031
ar.L12 -0.0401 0.010 -3.945 0.000 -0.060 -0.020
ar.L13 0.0171 0.015 1.160 0.246 -0.012 0.046
ar.L14 -0.0263 0.005 -5.547 0.000 -0.036 -0.017
ar.L15 -0.0132 0.008 -1.573 0.116 -0.030 0.003
ar.L16 -0.0098 0.008 -1.255 0.210 -0.025 0.005
ar.L17 0.0273 0.006 4.273 0.000 0.015 0.040
ar.L18 0.0052 0.007 0.787 0.431 -0.008 0.018
ar.L19 -0.0002 0.006 -0.038 0.970 -0.011 0.011
ar.L20 0.0026 0.004 0.662 0.508 -0.005 0.010
ar.L21 0.0213 0.005 4.533 0.000 0.012 0.031
ar.L22 -0.0032 0.007 -0.457 0.647 -0.017 0.011
ar.L23 -0.0035 0.005 -0.752 0.452 -0.013 0.006
ar.L24 0.0258 0.004 6.663 0.000 0.018 0.033
ar.L25 -0.0404 0.007 -5.587 0.000 -0.055 -0.026
ar.L26 0.0162 0.008 1.944 0.052 -0.000 0.033
ar.L27 0.0077 0.004 2.065 0.039 0.000 0.015
ar.L28 -0.0033 0.005 -0.638 0.524 -0.014 0.007
ar.L29 0.0028 0.005 0.596 0.551 -0.006 0.012
ar.L30 -0.0134 0.003 -4.084 0.000 -0.020 -0.007
ma.L1 -0.4191 0.258 -1.624 0.104 -0.925 0.087
sigma2 0.5217 0.001 386.592 0.000 0.519 0.524
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 3173787.80
Prob(Q): 0.95 Prob(JB): 0.00
Heteroskedasticity (H): 35.34 Skew: -0.27
Prob(H) (two-sided): 0.00 Kurtosis: 76.17
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 273.183997
14228 272.854999
14229 272.903290
14230 273.131643
14231 272.970326
14232 272.983452
14233 273.199602
14234 273.340249
14235 273.398163
14236 273.562832
Name: predicted_mean, dtype: float64
lower price upper price
14227 271.768378 274.599616
14228 270.903623 274.806375
14229 270.535842 275.270739
14230 270.411862 275.851423
14231 269.928718 276.011935
14232 269.635947 276.330957
14233 269.546235 276.852970
14234 269.394821 277.285678
14235 269.176682 277.619644
14236 269.077166 278.048499
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 2.3101290285547753
arima_forecast('DOHOL', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 13.19
2023-09-20 10:00:00+03:00 13.15
2023-09-20 11:00:00+03:00 13.04
2023-09-20 12:00:00+03:00 13.00
2023-09-20 13:00:00+03:00 12.94
2023-09-20 14:00:00+03:00 13.02
2023-09-20 15:00:00+03:00 13.00
2023-09-20 16:00:00+03:00 12.92
2023-09-20 17:00:00+03:00 12.87
2023-09-20 18:00:00+03:00 12.86
2023-09-21 09:00:00+03:00 12.84
2023-09-21 10:00:00+03:00 12.86
2023-09-21 11:00:00+03:00 12.94
2023-09-21 12:00:00+03:00 12.89
2023-09-21 13:00:00+03:00 12.87
2023-09-21 14:00:00+03:00 13.03
2023-09-21 15:00:00+03:00 13.00
2023-09-21 16:00:00+03:00 13.09
2023-09-21 17:00:00+03:00 13.28
2023-09-21 18:00:00+03:00 13.28
2023-09-22 09:00:00+03:00 13.28
2023-09-22 10:00:00+03:00 13.25
2023-09-22 11:00:00+03:00 13.32
2023-09-22 12:00:00+03:00 13.33
2023-09-22 13:00:00+03:00 13.32
2023-09-22 14:00:00+03:00 13.05
2023-09-22 15:00:00+03:00 13.06
2023-09-22 16:00:00+03:00 13.07
2023-09-22 17:00:00+03:00 13.05
2023-09-22 18:00:00+03:00 13.06
Mean of the first 10 values: price 13.307
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 13.14
2023-09-25 10:00:00+03:00 13.20
2023-09-25 11:00:00+03:00 13.19
2023-09-25 12:00:00+03:00 13.29
2023-09-25 13:00:00+03:00 13.32
2023-09-25 14:00:00+03:00 13.27
2023-09-25 15:00:00+03:00 13.31
2023-09-25 16:00:00+03:00 13.36
2023-09-25 17:00:00+03:00 13.50
2023-09-25 18:00:00+03:00 13.49
price
timestamp
2018-01-02 10:00:00+03:00 0.0082
2018-01-02 11:00:00+03:00 0.0000
2018-01-02 12:00:00+03:00 0.0000
2018-01-02 13:00:00+03:00 0.0079
2018-01-02 14:00:00+03:00 -0.0079
... ...
2023-09-22 14:00:00+03:00 -0.2700
2023-09-22 15:00:00+03:00 0.0100
2023-09-22 16:00:00+03:00 0.0100
2023-09-22 17:00:00+03:00 -0.0200
2023-09-22 18:00:00+03:00 0.0100
[14226 rows x 1 columns]
ADF Statistic: -18.874081160906577
p-value: 0.0
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 23923.644
Date: Sun, 24 Dec 2023 AIC -47783.288
Time: 01:27:34 BIC -47541.278
Sample: 0 HQIC -47702.784
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0088 0.550 0.016 0.987 -1.068 1.086
ar.L2 0.0261 0.010 2.685 0.007 0.007 0.045
ar.L3 0.0329 0.014 2.294 0.022 0.005 0.061
ar.L4 0.0106 0.019 0.567 0.571 -0.026 0.047
ar.L5 -0.0046 0.007 -0.701 0.483 -0.018 0.008
ar.L6 0.0234 0.004 5.506 0.000 0.015 0.032
ar.L7 -0.0176 0.013 -1.310 0.190 -0.044 0.009
ar.L8 -0.0027 0.010 -0.265 0.791 -0.023 0.017
ar.L9 0.0064 0.004 1.717 0.086 -0.001 0.014
ar.L10 -0.0485 0.005 -10.542 0.000 -0.058 -0.040
ar.L11 -0.0365 0.027 -1.360 0.174 -0.089 0.016
ar.L12 -0.0179 0.020 -0.881 0.378 -0.058 0.022
ar.L13 0.0249 0.010 2.427 0.015 0.005 0.045
ar.L14 -0.0054 0.014 -0.388 0.698 -0.033 0.022
ar.L15 -0.0314 0.005 -6.842 0.000 -0.040 -0.022
ar.L16 0.0160 0.018 0.900 0.368 -0.019 0.051
ar.L17 -0.0071 0.009 -0.761 0.447 -0.026 0.011
ar.L18 -0.0009 0.006 -0.154 0.877 -0.012 0.010
ar.L19 -0.0102 0.004 -2.656 0.008 -0.018 -0.003
ar.L20 0.0302 0.007 4.290 0.000 0.016 0.044
ar.L21 -0.0391 0.017 -2.324 0.020 -0.072 -0.006
ar.L22 0.0061 0.022 0.274 0.784 -0.037 0.049
ar.L23 0.0258 0.005 4.828 0.000 0.015 0.036
ar.L24 -0.0031 0.014 -0.217 0.828 -0.031 0.025
ar.L25 0.0205 0.004 4.794 0.000 0.012 0.029
ar.L26 -0.0177 0.012 -1.501 0.133 -0.041 0.005
ar.L27 0.0452 0.010 4.389 0.000 0.025 0.065
ar.L28 -0.0216 0.025 -0.859 0.390 -0.071 0.028
ar.L29 0.0154 0.013 1.219 0.223 -0.009 0.040
ar.L30 0.0066 0.009 0.756 0.450 -0.011 0.024
ma.L1 0.0075 0.549 0.014 0.989 -1.069 1.084
sigma2 0.0020 6.64e-06 305.113 0.000 0.002 0.002
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 625255.01
Prob(Q): 0.97 Prob(JB): 0.00
Heteroskedasticity (H): 58.18 Skew: 0.29
Prob(H) (two-sided): 0.00 Kurtosis: 35.47
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 13.055134
14228 13.052259
14229 13.053699
14230 13.039222
14231 13.036180
14232 13.057271
14233 13.057245
14234 13.060252
14235 13.060586
14236 13.057421
Name: predicted_mean, dtype: float64
lower price upper price
14227 12.966898 13.143370
14228 12.926453 13.178064
14229 12.897849 13.209549
14230 12.856727 13.221717
14231 12.829966 13.242395
14232 12.829894 13.284648
14233 12.809650 13.304840
14234 12.794500 13.326003
14235 12.777902 13.343270
14236 12.758562 13.356279
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.2775334440525403
arima_forecast('EKGYO', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 7.92
2023-09-20 10:00:00+03:00 8.00
2023-09-20 11:00:00+03:00 7.97
2023-09-20 12:00:00+03:00 7.95
2023-09-20 13:00:00+03:00 7.97
2023-09-20 14:00:00+03:00 8.01
2023-09-20 15:00:00+03:00 7.99
2023-09-20 16:00:00+03:00 7.94
2023-09-20 17:00:00+03:00 7.85
2023-09-20 18:00:00+03:00 7.80
2023-09-21 09:00:00+03:00 7.75
2023-09-21 10:00:00+03:00 7.82
2023-09-21 11:00:00+03:00 7.83
2023-09-21 12:00:00+03:00 7.78
2023-09-21 13:00:00+03:00 7.80
2023-09-21 14:00:00+03:00 7.92
2023-09-21 15:00:00+03:00 7.88
2023-09-21 16:00:00+03:00 7.91
2023-09-21 17:00:00+03:00 8.01
2023-09-21 18:00:00+03:00 8.03
2023-09-22 09:00:00+03:00 8.03
2023-09-22 10:00:00+03:00 8.02
2023-09-22 11:00:00+03:00 8.05
2023-09-22 12:00:00+03:00 8.03
2023-09-22 13:00:00+03:00 7.98
2023-09-22 14:00:00+03:00 7.91
2023-09-22 15:00:00+03:00 7.89
2023-09-22 16:00:00+03:00 7.89
2023-09-22 17:00:00+03:00 7.90
2023-09-22 18:00:00+03:00 7.91
Mean of the first 10 values: price 8.061
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 7.94
2023-09-25 10:00:00+03:00 8.08
2023-09-25 11:00:00+03:00 8.08
2023-09-25 12:00:00+03:00 8.06
2023-09-25 13:00:00+03:00 8.05
2023-09-25 14:00:00+03:00 8.00
2023-09-25 15:00:00+03:00 7.99
2023-09-25 16:00:00+03:00 8.05
2023-09-25 17:00:00+03:00 8.18
2023-09-25 18:00:00+03:00 8.18
price
timestamp
2018-01-02 10:00:00+03:00 0.0246
2018-01-02 11:00:00+03:00 -0.0081
2018-01-02 12:00:00+03:00 0.0081
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 0.0081
... ...
2023-09-22 14:00:00+03:00 -0.0700
2023-09-22 15:00:00+03:00 -0.0200
2023-09-22 16:00:00+03:00 0.0000
2023-09-22 17:00:00+03:00 0.0100
2023-09-22 18:00:00+03:00 0.0100
[14226 rows x 1 columns]
ADF Statistic: -16.268374896071755
p-value: 3.5083175413537314e-29
Critical Values: {'1%': -3.430811052000141, '5%': -2.8617437641803356, '10%': -2.566878458903999}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 27355.560
Date: Sun, 24 Dec 2023 AIC -54647.121
Time: 01:29:04 BIC -54405.110
Sample: 0 HQIC -54566.616
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0188 0.559 0.034 0.973 -1.076 1.114
ar.L2 0.0107 0.021 0.505 0.614 -0.031 0.052
ar.L3 -0.0026 0.007 -0.364 0.716 -0.016 0.011
ar.L4 0.0130 0.004 3.331 0.001 0.005 0.021
ar.L5 -0.0052 0.008 -0.665 0.506 -0.021 0.010
ar.L6 0.0249 0.005 5.099 0.000 0.015 0.034
ar.L7 -0.0128 0.015 -0.860 0.390 -0.042 0.016
ar.L8 0.0152 0.009 1.691 0.091 -0.002 0.033
ar.L9 0.0466 0.009 5.033 0.000 0.028 0.065
ar.L10 -0.0067 0.025 -0.264 0.792 -0.057 0.043
ar.L11 -0.0423 0.006 -7.677 0.000 -0.053 -0.032
ar.L12 -0.0221 0.024 -0.920 0.357 -0.069 0.025
ar.L13 -0.0182 0.012 -1.517 0.129 -0.042 0.005
ar.L14 -0.0027 0.011 -0.253 0.800 -0.024 0.018
ar.L15 0.0079 0.005 1.722 0.085 -0.001 0.017
ar.L16 -0.0004 0.006 -0.068 0.945 -0.013 0.012
ar.L17 -0.0132 0.004 -3.242 0.001 -0.021 -0.005
ar.L18 -0.0174 0.009 -2.035 0.042 -0.034 -0.001
ar.L19 0.0515 0.010 5.097 0.000 0.032 0.071
ar.L20 0.0106 0.030 0.358 0.720 -0.048 0.069
ar.L21 0.0189 0.006 3.042 0.002 0.007 0.031
ar.L22 0.0053 0.011 0.484 0.628 -0.016 0.027
ar.L23 -0.0054 0.005 -1.039 0.299 -0.016 0.005
ar.L24 0.0029 0.005 0.558 0.577 -0.007 0.013
ar.L25 -0.0137 0.004 -3.261 0.001 -0.022 -0.005
ar.L26 -0.0077 0.008 -0.909 0.364 -0.024 0.009
ar.L27 0.0236 0.006 3.695 0.000 0.011 0.036
ar.L28 -0.0186 0.015 -1.274 0.203 -0.047 0.010
ar.L29 0.0129 0.011 1.151 0.250 -0.009 0.035
ar.L30 -0.0061 0.008 -0.757 0.449 -0.022 0.010
ma.L1 0.0184 0.558 0.033 0.974 -1.076 1.113
sigma2 0.0013 3.69e-06 338.753 0.000 0.001 0.001
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1254139.08
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 25.79 Skew: 0.27
Prob(H) (two-sided): 0.00 Kurtosis: 48.99
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 7.909854
14228 7.908316
14229 7.904935
14230 7.902015
14231 7.905615
14232 7.904336
14233 7.909942
14234 7.917166
14235 7.924528
14236 7.925618
Name: predicted_mean, dtype: float64
lower price upper price
14227 7.840532 7.979175
14228 7.808444 8.008189
14229 7.781411 8.028460
14230 7.758759 8.045271
14231 7.744626 8.066603
14232 7.727513 8.081159
14233 7.717924 8.101960
14234 7.711361 8.122971
14235 7.705444 8.143612
14236 7.692939 8.158298
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.16432282382328908
arima_forecast('EREGL', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 42.34
2023-09-20 10:00:00+03:00 42.32
2023-09-20 11:00:00+03:00 42.00
2023-09-20 12:00:00+03:00 41.98
2023-09-20 13:00:00+03:00 41.94
2023-09-20 14:00:00+03:00 41.90
2023-09-20 15:00:00+03:00 41.80
2023-09-20 16:00:00+03:00 41.44
2023-09-20 17:00:00+03:00 41.08
2023-09-20 18:00:00+03:00 41.00
2023-09-21 09:00:00+03:00 40.78
2023-09-21 10:00:00+03:00 40.48
2023-09-21 11:00:00+03:00 40.48
2023-09-21 12:00:00+03:00 40.38
2023-09-21 13:00:00+03:00 40.42
2023-09-21 14:00:00+03:00 41.26
2023-09-21 15:00:00+03:00 41.10
2023-09-21 16:00:00+03:00 41.28
2023-09-21 17:00:00+03:00 41.72
2023-09-21 18:00:00+03:00 41.74
2023-09-22 09:00:00+03:00 41.84
2023-09-22 10:00:00+03:00 42.08
2023-09-22 11:00:00+03:00 42.18
2023-09-22 12:00:00+03:00 42.32
2023-09-22 13:00:00+03:00 42.14
2023-09-22 14:00:00+03:00 42.84
2023-09-22 15:00:00+03:00 43.06
2023-09-22 16:00:00+03:00 43.22
2023-09-22 17:00:00+03:00 43.18
2023-09-22 18:00:00+03:00 43.28
Mean of the first 10 values: price 45.784
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 43.44
2023-09-25 10:00:00+03:00 43.98
2023-09-25 11:00:00+03:00 45.16
2023-09-25 12:00:00+03:00 45.74
2023-09-25 13:00:00+03:00 46.80
2023-09-25 14:00:00+03:00 46.68
2023-09-25 15:00:00+03:00 46.60
2023-09-25 16:00:00+03:00 46.74
2023-09-25 17:00:00+03:00 46.38
2023-09-25 18:00:00+03:00 46.32
price
timestamp
2018-01-02 10:00:00+03:00 0.0178
2018-01-02 11:00:00+03:00 -0.0237
2018-01-02 12:00:00+03:00 0.0000
2018-01-02 13:00:00+03:00 0.0118
2018-01-02 14:00:00+03:00 0.0237
... ...
2023-09-22 14:00:00+03:00 0.7000
2023-09-22 15:00:00+03:00 0.2200
2023-09-22 16:00:00+03:00 0.1600
2023-09-22 17:00:00+03:00 -0.0400
2023-09-22 18:00:00+03:00 0.1000
[14226 rows x 1 columns]
ADF Statistic: -18.459300597620004
p-value: 2.147942075114296e-30
Critical Values: {'1%': -3.430811052000141, '5%': -2.8617437641803356, '10%': -2.566878458903999}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 4151.688
Date: Sun, 24 Dec 2023 AIC -8239.376
Time: 01:31:40 BIC -7997.365
Sample: 0 HQIC -8158.871
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.5443 0.174 3.125 0.002 0.203 0.886
ar.L2 -0.0439 0.008 -5.169 0.000 -0.061 -0.027
ar.L3 0.0733 0.005 13.326 0.000 0.062 0.084
ar.L4 -0.0339 0.011 -2.959 0.003 -0.056 -0.011
ar.L5 -0.0156 0.004 -3.737 0.000 -0.024 -0.007
ar.L6 0.0134 0.006 2.192 0.028 0.001 0.025
ar.L7 -0.0066 0.005 -1.317 0.188 -0.017 0.003
ar.L8 0.0188 0.005 3.921 0.000 0.009 0.028
ar.L9 0.0239 0.005 4.460 0.000 0.013 0.034
ar.L10 -0.0282 0.007 -3.840 0.000 -0.043 -0.014
ar.L11 -0.0537 0.005 -11.201 0.000 -0.063 -0.044
ar.L12 -0.0052 0.011 -0.461 0.645 -0.027 0.017
ar.L13 0.0697 0.008 8.887 0.000 0.054 0.085
ar.L14 -0.0152 0.010 -1.473 0.141 -0.035 0.005
ar.L15 0.0032 0.005 0.654 0.513 -0.006 0.013
ar.L16 0.0029 0.006 0.517 0.605 -0.008 0.014
ar.L17 -0.0222 0.005 -4.349 0.000 -0.032 -0.012
ar.L18 0.0071 0.006 1.242 0.214 -0.004 0.018
ar.L19 -0.0009 0.006 -0.157 0.875 -0.012 0.010
ar.L20 0.0098 0.005 2.093 0.036 0.001 0.019
ar.L21 0.0031 0.004 0.769 0.442 -0.005 0.011
ar.L22 -0.0325 0.005 -5.951 0.000 -0.043 -0.022
ar.L23 0.0105 0.007 1.586 0.113 -0.002 0.023
ar.L24 -0.0036 0.005 -0.716 0.474 -0.014 0.006
ar.L25 -0.0086 0.005 -1.699 0.089 -0.019 0.001
ar.L26 -0.0086 0.006 -1.482 0.138 -0.020 0.003
ar.L27 0.0298 0.005 5.467 0.000 0.019 0.041
ar.L28 -0.0086 0.007 -1.293 0.196 -0.022 0.004
ar.L29 0.0149 0.005 2.834 0.005 0.005 0.025
ar.L30 0.0089 0.007 1.310 0.190 -0.004 0.022
ma.L1 -0.5014 0.174 -2.877 0.004 -0.843 -0.160
sigma2 0.0327 8.9e-05 367.097 0.000 0.032 0.033
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 2857002.15
Prob(Q): 0.97 Prob(JB): 0.00
Heteroskedasticity (H): 44.64 Skew: 0.68
Prob(H) (two-sided): 0.00 Kurtosis: 72.41
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 43.300891
14228 43.319386
14229 43.299896
14230 43.313855
14231 43.339047
14232 43.349971
14233 43.325397
14234 43.241001
14235 43.245443
14236 43.242639
Name: predicted_mean, dtype: float64
lower price upper price
14227 42.946688 43.655094
14228 42.807600 43.831173
14229 42.672945 43.926848
14230 42.578938 44.048773
14231 42.509639 44.168454
14232 42.438698 44.261244
14233 42.338083 44.312711
14234 42.183471 44.298531
14235 42.120407 44.370478
14236 42.049925 44.435352
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 2.7410005144731233
arima_forecast('FROTO', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 814.0292
2023-09-20 10:00:00+03:00 824.0647
2023-09-20 11:00:00+03:00 811.2309
2023-09-20 12:00:00+03:00 806.5992
2023-09-20 13:00:00+03:00 806.9851
2023-09-20 14:00:00+03:00 805.2482
2023-09-20 15:00:00+03:00 800.3270
2023-09-20 16:00:00+03:00 796.2742
2023-09-20 17:00:00+03:00 796.8532
2023-09-20 18:00:00+03:00 797.2392
2023-09-21 09:00:00+03:00 797.1427
2023-09-21 10:00:00+03:00 789.1336
2023-09-21 11:00:00+03:00 790.9670
2023-09-21 12:00:00+03:00 790.9670
2023-09-21 13:00:00+03:00 795.5988
2023-09-21 14:00:00+03:00 807.5641
2023-09-21 15:00:00+03:00 803.8973
2023-09-21 16:00:00+03:00 806.9851
2023-09-21 17:00:00+03:00 819.5294
2023-09-21 18:00:00+03:00 819.7224
2023-09-22 09:00:00+03:00 819.7224
2023-09-22 10:00:00+03:00 815.4767
2023-09-22 11:00:00+03:00 816.7311
2023-09-22 12:00:00+03:00 818.6610
2023-09-22 13:00:00+03:00 815.0907
2023-09-22 14:00:00+03:00 810.1695
2023-09-22 15:00:00+03:00 808.0466
2023-09-22 16:00:00+03:00 809.2045
2023-09-22 17:00:00+03:00 812.4853
2023-09-22 18:00:00+03:00 809.9765
Mean of the first 10 values: price 812.85202
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 813.8363
2023-09-25 10:00:00+03:00 804.9587
2023-09-25 11:00:00+03:00 806.6957
2023-09-25 12:00:00+03:00 812.0994
2023-09-25 13:00:00+03:00 811.1344
2023-09-25 14:00:00+03:00 805.7307
2023-09-25 15:00:00+03:00 809.3010
2023-09-25 16:00:00+03:00 823.9682
2023-09-25 17:00:00+03:00 820.3979
2023-09-25 18:00:00+03:00 820.3979
price
timestamp
2018-01-02 10:00:00+03:00 0.2094
2018-01-02 11:00:00+03:00 0.3142
2018-01-02 12:00:00+03:00 0.1744
2018-01-02 13:00:00+03:00 -0.1395
2018-01-02 14:00:00+03:00 0.2791
... ...
2023-09-22 14:00:00+03:00 -4.9212
2023-09-22 15:00:00+03:00 -2.1229
2023-09-22 16:00:00+03:00 1.1579
2023-09-22 17:00:00+03:00 3.2808
2023-09-22 18:00:00+03:00 -2.5088
[14225 rows x 1 columns]
ADF Statistic: -16.81526382151908
p-value: 1.1901683567553043e-29
Critical Values: {'1%': -3.43081118206233, '5%': -2.8617438216574906, '10%': -2.5668784894985173}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14226
Model: ARIMA(30, 1, 1) Log Likelihood -32308.628
Date: Sun, 24 Dec 2023 AIC 64681.257
Time: 01:33:31 BIC 64923.265
Sample: 0 HQIC 64761.761
- 14226
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.4709 0.156 3.027 0.002 0.166 0.776
ar.L2 -0.0015 0.006 -0.257 0.797 -0.013 0.010
ar.L3 0.0100 0.005 2.074 0.038 0.001 0.019
ar.L4 0.0010 0.004 0.245 0.806 -0.007 0.009
ar.L5 0.0201 0.004 5.077 0.000 0.012 0.028
ar.L6 0.0397 0.006 6.616 0.000 0.028 0.051
ar.L7 -0.0262 0.009 -2.841 0.005 -0.044 -0.008
ar.L8 0.0032 0.005 0.691 0.490 -0.006 0.012
ar.L9 0.0407 0.004 9.344 0.000 0.032 0.049
ar.L10 -0.0092 0.008 -1.223 0.221 -0.024 0.006
ar.L11 -0.0509 0.004 -12.793 0.000 -0.059 -0.043
ar.L12 0.0006 0.008 0.067 0.946 -0.016 0.017
ar.L13 -0.0179 0.006 -3.205 0.001 -0.029 -0.007
ar.L14 0.0280 0.006 4.584 0.000 0.016 0.040
ar.L15 -0.0064 0.005 -1.341 0.180 -0.016 0.003
ar.L16 -0.0065 0.004 -1.530 0.126 -0.015 0.002
ar.L17 0.0294 0.004 6.794 0.000 0.021 0.038
ar.L18 -0.0282 0.006 -4.633 0.000 -0.040 -0.016
ar.L19 0.0002 0.005 0.034 0.973 -0.010 0.011
ar.L20 0.0066 0.004 1.499 0.134 -0.002 0.015
ar.L21 -0.0094 0.005 -2.059 0.040 -0.018 -0.000
ar.L22 0.0220 0.005 4.470 0.000 0.012 0.032
ar.L23 -0.0498 0.005 -9.267 0.000 -0.060 -0.039
ar.L24 0.0120 0.007 1.705 0.088 -0.002 0.026
ar.L25 -0.0025 0.005 -0.494 0.621 -0.013 0.007
ar.L26 -0.0040 0.005 -0.808 0.419 -0.014 0.006
ar.L27 0.0216 0.005 4.307 0.000 0.012 0.031
ar.L28 -0.0190 0.006 -3.208 0.001 -0.031 -0.007
ar.L29 0.0310 0.004 6.948 0.000 0.022 0.040
ar.L30 0.0055 0.006 0.918 0.359 -0.006 0.017
ma.L1 -0.4964 0.156 -3.190 0.001 -0.801 -0.191
sigma2 5.4995 0.016 337.019 0.000 5.468 5.532
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1366530.73
Prob(Q): 0.95 Prob(JB): 0.00
Heteroskedasticity (H): 132.03 Skew: 1.99
Prob(H) (two-sided): 0.00 Kurtosis: 50.85
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14226 809.659274
14227 809.096599
14228 809.567163
14229 808.910408
14230 808.981010
14231 808.885828
14232 808.734535
14233 809.114563
14234 808.231907
14235 808.142939
Name: predicted_mean, dtype: float64
lower price upper price
14226 805.062947 814.255602
14227 802.678787 815.514410
14228 801.776319 817.358007
14229 799.946368 817.874447
14230 798.975470 818.986550
14231 797.897657 819.874000
14232 796.756576 820.712494
14233 796.230032 821.999095
14234 794.498116 821.965698
14235 793.544460 822.741419
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 7.6597877426069605
arima_forecast('GUBRF', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 342.5
2023-09-20 10:00:00+03:00 342.9
2023-09-20 11:00:00+03:00 340.3
2023-09-20 12:00:00+03:00 339.5
2023-09-20 13:00:00+03:00 337.7
2023-09-20 14:00:00+03:00 337.5
2023-09-20 15:00:00+03:00 333.9
2023-09-20 16:00:00+03:00 329.5
2023-09-20 17:00:00+03:00 330.0
2023-09-20 18:00:00+03:00 328.5
2023-09-21 09:00:00+03:00 328.0
2023-09-21 10:00:00+03:00 328.6
2023-09-21 11:00:00+03:00 331.6
2023-09-21 12:00:00+03:00 329.6
2023-09-21 13:00:00+03:00 328.8
2023-09-21 14:00:00+03:00 332.8
2023-09-21 15:00:00+03:00 335.4
2023-09-21 16:00:00+03:00 337.7
2023-09-21 17:00:00+03:00 340.6
2023-09-21 18:00:00+03:00 340.5
2023-09-22 09:00:00+03:00 341.5
2023-09-22 10:00:00+03:00 339.6
2023-09-22 11:00:00+03:00 340.6
2023-09-22 12:00:00+03:00 338.7
2023-09-22 13:00:00+03:00 339.7
2023-09-22 14:00:00+03:00 337.2
2023-09-22 15:00:00+03:00 337.9
2023-09-22 16:00:00+03:00 337.4
2023-09-22 17:00:00+03:00 339.7
2023-09-22 18:00:00+03:00 339.5
Mean of the first 10 values: price 344.27
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 342.0
2023-09-25 10:00:00+03:00 342.7
2023-09-25 11:00:00+03:00 342.5
2023-09-25 12:00:00+03:00 342.4
2023-09-25 13:00:00+03:00 342.3
2023-09-25 14:00:00+03:00 340.7
2023-09-25 15:00:00+03:00 345.7
2023-09-25 16:00:00+03:00 347.9
2023-09-25 17:00:00+03:00 348.0
2023-09-25 18:00:00+03:00 348.5
price
timestamp
2018-01-02 10:00:00+03:00 -0.01
2018-01-02 11:00:00+03:00 0.03
2018-01-02 12:00:00+03:00 0.03
2018-01-02 13:00:00+03:00 0.01
2018-01-02 14:00:00+03:00 0.03
... ...
2023-09-22 14:00:00+03:00 -2.50
2023-09-22 15:00:00+03:00 0.70
2023-09-22 16:00:00+03:00 -0.50
2023-09-22 17:00:00+03:00 2.30
2023-09-22 18:00:00+03:00 -0.20
[14217 rows x 1 columns]
ADF Statistic: -14.876109874306591
p-value: 1.6356253220281786e-27
Critical Values: {'1%': -3.4308114098478204, '5%': -2.8617439223205756, '10%': -2.566878543080479}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14218
Model: ARIMA(30, 1, 1) Log Likelihood -23328.547
Date: Sun, 24 Dec 2023 AIC 46721.095
Time: 18:27:26 BIC 46963.085
Sample: 0 HQIC 46801.595
- 14218
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.1915 0.087 -2.201 0.028 -0.362 -0.021
ar.L2 -0.0269 0.004 -6.076 0.000 -0.036 -0.018
ar.L3 0.0123 0.005 2.585 0.010 0.003 0.022
ar.L4 -0.0017 0.004 -0.421 0.674 -0.010 0.006
ar.L5 0.0113 0.004 3.194 0.001 0.004 0.018
ar.L6 0.0250 0.003 7.369 0.000 0.018 0.032
ar.L7 0.0248 0.005 5.411 0.000 0.016 0.034
ar.L8 0.0008 0.004 0.187 0.851 -0.008 0.009
ar.L9 -0.0118 0.004 -3.180 0.001 -0.019 -0.005
ar.L10 0.0591 0.003 20.924 0.000 0.054 0.065
ar.L11 0.0124 0.007 1.817 0.069 -0.001 0.026
ar.L12 0.0137 0.004 3.508 0.000 0.006 0.021
ar.L13 0.0380 0.003 12.046 0.000 0.032 0.044
ar.L14 0.0238 0.005 5.214 0.000 0.015 0.033
ar.L15 0.0257 0.003 7.461 0.000 0.019 0.032
ar.L16 -0.0064 0.004 -1.536 0.125 -0.014 0.002
ar.L17 0.0125 0.004 3.324 0.001 0.005 0.020
ar.L18 0.0125 0.004 3.138 0.002 0.005 0.020
ar.L19 0.0207 0.004 5.344 0.000 0.013 0.028
ar.L20 0.0201 0.004 5.688 0.000 0.013 0.027
ar.L21 -0.0009 0.004 -0.236 0.813 -0.008 0.007
ar.L22 -0.0145 0.004 -3.648 0.000 -0.022 -0.007
ar.L23 0.0120 0.004 3.173 0.002 0.005 0.019
ar.L24 -0.0091 0.004 -2.418 0.016 -0.016 -0.002
ar.L25 -0.0083 0.004 -2.119 0.034 -0.016 -0.001
ar.L26 0.0263 0.004 7.064 0.000 0.019 0.034
ar.L27 0.0072 0.004 1.657 0.097 -0.001 0.016
ar.L28 -0.0037 0.003 -1.153 0.249 -0.010 0.003
ar.L29 0.0606 0.003 19.484 0.000 0.054 0.067
ar.L30 0.0447 0.006 8.015 0.000 0.034 0.056
ma.L1 0.2221 0.087 2.549 0.011 0.051 0.393
sigma2 1.5587 0.004 365.702 0.000 1.550 1.567
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1739529.44
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 5470.25 Skew: 0.27
Prob(H) (two-sided): 0.00 Kurtosis: 57.19
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14218 339.693737
14219 339.735567
14220 339.671875
14221 339.404602
14222 339.523362
14223 339.427161
14224 339.001256
14225 338.932304
14226 339.060881
14227 338.880617
Name: predicted_mean, dtype: float64
lower price upper price
14218 337.246730 342.140743
14219 336.221546 343.249587
14220 335.392760 343.950991
14221 334.456130 344.353074
14222 333.990068 345.056656
14223 333.354065 345.500256
14224 332.410390 345.592122
14225 331.843372 346.021236
14226 331.509524 346.612239
14227 330.902967 346.858266
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 5.8149838303841515
arima_forecast('GARAN', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 50.05
2023-09-20 10:00:00+03:00 51.15
2023-09-20 11:00:00+03:00 52.25
2023-09-20 12:00:00+03:00 52.00
2023-09-20 13:00:00+03:00 52.25
2023-09-20 14:00:00+03:00 52.25
2023-09-20 15:00:00+03:00 52.45
2023-09-20 16:00:00+03:00 52.45
2023-09-20 17:00:00+03:00 51.35
2023-09-20 18:00:00+03:00 51.50
2023-09-21 09:00:00+03:00 51.50
2023-09-21 10:00:00+03:00 52.25
2023-09-21 11:00:00+03:00 52.45
2023-09-21 12:00:00+03:00 52.40
2023-09-21 13:00:00+03:00 52.90
2023-09-21 14:00:00+03:00 51.20
2023-09-21 15:00:00+03:00 51.15
2023-09-21 16:00:00+03:00 51.90
2023-09-21 17:00:00+03:00 51.85
2023-09-21 18:00:00+03:00 52.00
2023-09-22 09:00:00+03:00 52.00
2023-09-22 10:00:00+03:00 51.60
2023-09-22 11:00:00+03:00 51.75
2023-09-22 12:00:00+03:00 51.75
2023-09-22 13:00:00+03:00 52.05
2023-09-22 14:00:00+03:00 51.30
2023-09-22 15:00:00+03:00 51.25
2023-09-22 16:00:00+03:00 51.45
2023-09-22 17:00:00+03:00 50.85
2023-09-22 18:00:00+03:00 50.80
Mean of the first 10 values: price 51.44
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 50.95
2023-09-25 10:00:00+03:00 51.05
2023-09-25 11:00:00+03:00 51.20
2023-09-25 12:00:00+03:00 51.35
2023-09-25 13:00:00+03:00 51.85
2023-09-25 14:00:00+03:00 51.40
2023-09-25 15:00:00+03:00 51.40
2023-09-25 16:00:00+03:00 52.05
2023-09-25 17:00:00+03:00 51.60
2023-09-25 18:00:00+03:00 51.55
price
timestamp
2018-01-02 10:00:00+03:00 0.1110
2018-01-02 11:00:00+03:00 0.0257
2018-01-02 12:00:00+03:00 -0.0172
2018-01-02 13:00:00+03:00 0.0086
2018-01-02 14:00:00+03:00 0.0086
... ...
2023-09-22 14:00:00+03:00 -0.7500
2023-09-22 15:00:00+03:00 -0.0500
2023-09-22 16:00:00+03:00 0.2000
2023-09-22 17:00:00+03:00 -0.6000
2023-09-22 18:00:00+03:00 -0.0500
[14226 rows x 1 columns]
ADF Statistic: -18.961120996132177
p-value: 0.0
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 4789.822
Date: Sun, 24 Dec 2023 AIC -9515.644
Time: 01:34:19 BIC -9273.633
Sample: 0 HQIC -9435.139
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.0050 5.320 -0.001 0.999 -10.432 10.422
ar.L2 0.0155 0.055 0.279 0.780 -0.093 0.124
ar.L3 0.0290 0.082 0.354 0.723 -0.132 0.190
ar.L4 -0.0075 0.155 -0.048 0.961 -0.311 0.296
ar.L5 0.0275 0.039 0.697 0.486 -0.050 0.105
ar.L6 -0.0057 0.146 -0.039 0.969 -0.292 0.281
ar.L7 0.0050 0.030 0.168 0.867 -0.053 0.063
ar.L8 0.0001 0.027 0.005 0.996 -0.052 0.052
ar.L9 0.0336 0.004 9.116 0.000 0.026 0.041
ar.L10 0.0279 0.178 0.157 0.876 -0.322 0.378
ar.L11 -0.0066 0.150 -0.044 0.965 -0.300 0.287
ar.L12 -0.0150 0.035 -0.433 0.665 -0.083 0.053
ar.L13 -0.0082 0.079 -0.103 0.918 -0.164 0.148
ar.L14 -0.0015 0.044 -0.033 0.973 -0.088 0.085
ar.L15 0.0064 0.009 0.732 0.464 -0.011 0.023
ar.L16 -0.0106 0.034 -0.317 0.751 -0.076 0.055
ar.L17 0.0472 0.056 0.841 0.400 -0.063 0.157
ar.L18 -0.0039 0.251 -0.015 0.988 -0.496 0.488
ar.L19 0.0219 0.019 1.128 0.259 -0.016 0.060
ar.L20 -0.0191 0.116 -0.164 0.870 -0.247 0.209
ar.L21 -0.0100 0.101 -0.098 0.922 -0.209 0.189
ar.L22 0.0014 0.054 0.027 0.979 -0.104 0.107
ar.L23 0.0038 0.008 0.459 0.646 -0.012 0.020
ar.L24 -0.0173 0.021 -0.819 0.413 -0.059 0.024
ar.L25 -0.0054 0.092 -0.059 0.953 -0.186 0.175
ar.L26 0.0148 0.030 0.501 0.617 -0.043 0.073
ar.L27 0.0007 0.079 0.009 0.993 -0.154 0.155
ar.L28 -0.0197 0.005 -3.689 0.000 -0.030 -0.009
ar.L29 0.0027 0.105 0.026 0.979 -0.203 0.208
ar.L30 -0.0007 0.015 -0.047 0.962 -0.029 0.028
ma.L1 -0.0054 5.320 -0.001 0.999 -10.432 10.421
sigma2 0.0299 7.83e-05 381.168 0.000 0.030 0.030
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1535050.02
Prob(Q): 0.98 Prob(JB): 0.00
Heteroskedasticity (H): 25.71 Skew: 0.06
Prob(H) (two-sided): 0.00 Kurtosis: 53.89
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 50.730948
14228 50.740895
14229 50.677426
14230 50.684403
14231 50.644166
14232 50.621974
14233 50.681738
14234 50.666349
14235 50.620367
14236 50.639842
Name: predicted_mean, dtype: float64
lower price upper price
14227 50.392279 51.069616
14228 50.264428 51.217362
14229 50.091851 51.263000
14230 50.002124 51.366683
14231 49.878471 51.409861
14232 49.777059 51.466890
14233 49.765147 51.598328
14234 49.682620 51.650077
14235 49.573618 51.667117
14236 49.529996 51.749687
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.8448343909255085
arima_forecast('KRDMD', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 26.00
2023-09-20 10:00:00+03:00 26.12
2023-09-20 11:00:00+03:00 25.82
2023-09-20 12:00:00+03:00 26.06
2023-09-20 13:00:00+03:00 26.02
2023-09-20 14:00:00+03:00 26.20
2023-09-20 15:00:00+03:00 26.12
2023-09-20 16:00:00+03:00 25.68
2023-09-20 17:00:00+03:00 25.58
2023-09-20 18:00:00+03:00 25.60
2023-09-21 09:00:00+03:00 25.50
2023-09-21 10:00:00+03:00 25.36
2023-09-21 11:00:00+03:00 25.46
2023-09-21 12:00:00+03:00 25.34
2023-09-21 13:00:00+03:00 25.58
2023-09-21 14:00:00+03:00 26.06
2023-09-21 15:00:00+03:00 25.96
2023-09-21 16:00:00+03:00 26.22
2023-09-21 17:00:00+03:00 26.44
2023-09-21 18:00:00+03:00 26.46
2023-09-22 09:00:00+03:00 26.40
2023-09-22 10:00:00+03:00 26.60
2023-09-22 11:00:00+03:00 26.78
2023-09-22 12:00:00+03:00 26.54
2023-09-22 13:00:00+03:00 26.50
2023-09-22 14:00:00+03:00 27.32
2023-09-22 15:00:00+03:00 27.40
2023-09-22 16:00:00+03:00 27.56
2023-09-22 17:00:00+03:00 27.62
2023-09-22 18:00:00+03:00 27.52
Mean of the first 10 values: price 29.704
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 27.64
2023-09-25 10:00:00+03:00 28.60
2023-09-25 11:00:00+03:00 29.48
2023-09-25 12:00:00+03:00 29.76
2023-09-25 13:00:00+03:00 30.26
2023-09-25 14:00:00+03:00 30.26
2023-09-25 15:00:00+03:00 30.26
2023-09-25 16:00:00+03:00 30.26
2023-09-25 17:00:00+03:00 30.26
2023-09-25 18:00:00+03:00 30.26
price
timestamp
2018-01-02 10:00:00+03:00 -0.0166
2018-01-02 11:00:00+03:00 0.0415
2018-01-02 12:00:00+03:00 -0.0249
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 0.0083
... ...
2023-09-22 14:00:00+03:00 0.8200
2023-09-22 15:00:00+03:00 0.0800
2023-09-22 16:00:00+03:00 0.1600
2023-09-22 17:00:00+03:00 0.0600
2023-09-22 18:00:00+03:00 -0.1000
[14226 rows x 1 columns]
ADF Statistic: -17.01210552725342
p-value: 8.576566083830768e-30
Critical Values: {'1%': -3.430811052000141, '5%': -2.8617437641803356, '10%': -2.566878458903999}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 11905.061
Date: Sun, 24 Dec 2023 AIC -23746.122
Time: 01:36:15 BIC -23504.111
Sample: 0 HQIC -23665.617
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.4050 0.200 2.024 0.043 0.013 0.797
ar.L2 -0.0285 0.011 -2.540 0.011 -0.050 -0.007
ar.L3 0.0454 0.005 9.350 0.000 0.036 0.055
ar.L4 -0.0059 0.010 -0.615 0.538 -0.025 0.013
ar.L5 -0.0212 0.004 -4.941 0.000 -0.030 -0.013
ar.L6 0.0077 0.006 1.373 0.170 -0.003 0.019
ar.L7 0.0015 0.004 0.344 0.731 -0.007 0.010
ar.L8 -0.0057 0.005 -1.170 0.242 -0.015 0.004
ar.L9 0.0429 0.005 9.308 0.000 0.034 0.052
ar.L10 -0.0331 0.009 -3.578 0.000 -0.051 -0.015
ar.L11 -0.0321 0.006 -5.311 0.000 -0.044 -0.020
ar.L12 0.0061 0.009 0.682 0.495 -0.011 0.024
ar.L13 0.0089 0.005 1.795 0.073 -0.001 0.019
ar.L14 0.0124 0.005 2.541 0.011 0.003 0.022
ar.L15 -0.0256 0.005 -5.203 0.000 -0.035 -0.016
ar.L16 0.0176 0.006 3.082 0.002 0.006 0.029
ar.L17 -0.0132 0.005 -2.636 0.008 -0.023 -0.003
ar.L18 0.0053 0.005 1.038 0.299 -0.005 0.015
ar.L19 0.0417 0.005 8.202 0.000 0.032 0.052
ar.L20 -0.0090 0.009 -0.995 0.320 -0.027 0.009
ar.L21 -0.0076 0.004 -1.777 0.076 -0.016 0.001
ar.L22 0.0039 0.005 0.782 0.434 -0.006 0.014
ar.L23 -0.0025 0.005 -0.542 0.588 -0.012 0.007
ar.L24 4.837e-05 0.005 0.010 0.992 -0.010 0.010
ar.L25 0.0279 0.005 6.025 0.000 0.019 0.037
ar.L26 -0.0360 0.006 -5.769 0.000 -0.048 -0.024
ar.L27 0.0069 0.007 0.995 0.320 -0.007 0.020
ar.L28 -0.0190 0.004 -4.313 0.000 -0.028 -0.010
ar.L29 0.0243 0.006 3.980 0.000 0.012 0.036
ar.L30 0.0098 0.006 1.615 0.106 -0.002 0.022
ma.L1 -0.3529 0.200 -1.763 0.078 -0.745 0.039
sigma2 0.0110 3.28e-05 334.227 0.000 0.011 0.011
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1221518.35
Prob(Q): 0.97 Prob(JB): 0.00
Heteroskedasticity (H): 45.46 Skew: 0.57
Prob(H) (two-sided): 0.00 Kurtosis: 48.38
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 27.513486
14228 27.523761
14229 27.478515
14230 27.480302
14231 27.557107
14232 27.558100
14233 27.527989
14234 27.525918
14235 27.516256
14236 27.526801
Name: predicted_mean, dtype: float64
lower price upper price
14227 27.308117 27.718855
14228 27.225657 27.821865
14229 27.111225 27.845805
14230 27.050637 27.909966
14231 27.071781 28.042433
14232 27.024385 28.091815
14233 26.949845 28.106133
14234 26.906282 28.145554
14235 26.858161 28.174351
14236 26.829692 28.223910
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 2.343517765674792
arima_forecast('KCHOL', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 134.8
2023-09-20 10:00:00+03:00 136.9
2023-09-20 11:00:00+03:00 136.4
2023-09-20 12:00:00+03:00 136.4
2023-09-20 13:00:00+03:00 136.8
2023-09-20 14:00:00+03:00 136.5
2023-09-20 15:00:00+03:00 136.0
2023-09-20 16:00:00+03:00 135.9
2023-09-20 17:00:00+03:00 134.2
2023-09-20 18:00:00+03:00 134.6
2023-09-21 09:00:00+03:00 134.1
2023-09-21 10:00:00+03:00 133.7
2023-09-21 11:00:00+03:00 133.7
2023-09-21 12:00:00+03:00 133.7
2023-09-21 13:00:00+03:00 134.5
2023-09-21 14:00:00+03:00 136.9
2023-09-21 15:00:00+03:00 137.0
2023-09-21 16:00:00+03:00 136.9
2023-09-21 17:00:00+03:00 138.7
2023-09-21 18:00:00+03:00 138.8
2023-09-22 09:00:00+03:00 139.3
2023-09-22 10:00:00+03:00 140.6
2023-09-22 11:00:00+03:00 139.3
2023-09-22 12:00:00+03:00 140.0
2023-09-22 13:00:00+03:00 139.8
2023-09-22 14:00:00+03:00 138.8
2023-09-22 15:00:00+03:00 138.9
2023-09-22 16:00:00+03:00 139.0
2023-09-22 17:00:00+03:00 137.3
2023-09-22 18:00:00+03:00 137.5
Mean of the first 10 values: price 141.23
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 138.0
2023-09-25 10:00:00+03:00 139.8
2023-09-25 11:00:00+03:00 139.9
2023-09-25 12:00:00+03:00 141.2
2023-09-25 13:00:00+03:00 141.9
2023-09-25 14:00:00+03:00 142.2
2023-09-25 15:00:00+03:00 142.2
2023-09-25 16:00:00+03:00 142.8
2023-09-25 17:00:00+03:00 142.3
2023-09-25 18:00:00+03:00 142.0
price
timestamp
2018-01-02 10:00:00+03:00 -0.0262
2018-01-02 11:00:00+03:00 0.2524
2018-01-02 12:00:00+03:00 -0.0870
2018-01-02 13:00:00+03:00 0.0782
2018-01-02 14:00:00+03:00 0.1305
... ...
2023-09-22 14:00:00+03:00 -1.0000
2023-09-22 15:00:00+03:00 0.1000
2023-09-22 16:00:00+03:00 0.1000
2023-09-22 17:00:00+03:00 -1.7000
2023-09-22 18:00:00+03:00 0.2000
[14226 rows x 1 columns]
ADF Statistic: -18.49540065440885
p-value: 2.124049178894633e-30
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood -6813.630
Date: Sun, 24 Dec 2023 AIC 13691.261
Time: 16:27:47 BIC 13933.271
Sample: 0 HQIC 13771.765
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.8445 0.043 -19.504 0.000 -0.929 -0.760
ar.L2 -0.0314 0.006 -5.666 0.000 -0.042 -0.021
ar.L3 0.0318 0.005 6.110 0.000 0.022 0.042
ar.L4 0.0090 0.005 1.849 0.064 -0.001 0.018
ar.L5 -0.0003 0.005 -0.064 0.949 -0.009 0.009
ar.L6 0.0618 0.005 12.981 0.000 0.052 0.071
ar.L7 0.0164 0.006 2.891 0.004 0.005 0.027
ar.L8 -0.0032 0.006 -0.555 0.579 -0.015 0.008
ar.L9 0.0339 0.005 7.059 0.000 0.024 0.043
ar.L10 0.0209 0.004 5.378 0.000 0.013 0.029
ar.L11 -0.0268 0.004 -6.769 0.000 -0.035 -0.019
ar.L12 -0.0271 0.005 -5.668 0.000 -0.036 -0.018
ar.L13 0.0097 0.005 1.878 0.060 -0.000 0.020
ar.L14 0.0317 0.005 5.966 0.000 0.021 0.042
ar.L15 0.0030 0.005 0.599 0.549 -0.007 0.013
ar.L16 -0.0276 0.005 -5.660 0.000 -0.037 -0.018
ar.L17 0.0233 0.005 4.629 0.000 0.013 0.033
ar.L18 0.0062 0.005 1.168 0.243 -0.004 0.017
ar.L19 -0.0437 0.004 -11.102 0.000 -0.051 -0.036
ar.L20 -0.0570 0.005 -12.482 0.000 -0.066 -0.048
ar.L21 -0.0337 0.005 -6.487 0.000 -0.044 -0.023
ar.L22 0.0265 0.005 4.923 0.000 0.016 0.037
ar.L23 0.0095 0.005 1.771 0.077 -0.001 0.020
ar.L24 -0.0047 0.005 -0.882 0.378 -0.015 0.006
ar.L25 0.0250 0.005 5.182 0.000 0.016 0.034
ar.L26 0.0244 0.005 4.694 0.000 0.014 0.035
ar.L27 0.0127 0.005 2.315 0.021 0.002 0.023
ar.L28 0.0198 0.006 3.560 0.000 0.009 0.031
ar.L29 0.0126 0.005 2.712 0.007 0.003 0.022
ar.L30 -0.0176 0.004 -4.571 0.000 -0.025 -0.010
ma.L1 0.7949 0.043 18.477 0.000 0.711 0.879
sigma2 0.1526 0.000 357.999 0.000 0.152 0.153
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 2839473.97
Prob(Q): 0.96 Prob(JB): 0.00
Heteroskedasticity (H): 51.02 Skew: -0.66
Prob(H) (two-sided): 0.00 Kurtosis: 72.20
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 137.384395
14228 137.265869
14229 137.425405
14230 137.240580
14231 137.033361
14232 137.046422
14233 136.941715
14234 136.940253
14235 136.910920
14236 136.792110
Name: predicted_mean, dtype: float64
lower price upper price
14227 136.618781 138.150009
14228 136.209605 138.322133
14229 136.138182 138.712629
14230 135.748561 138.732599
14231 135.366124 138.700598
14232 135.217472 138.875371
14233 134.948030 138.935400
14234 134.803701 139.076806
14235 134.634424 139.187415
14236 134.378979 139.205241
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 4.438417034228074
arima_forecast('KOZAL', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 28.14
2023-09-20 10:00:00+03:00 28.70
2023-09-20 11:00:00+03:00 28.28
2023-09-20 12:00:00+03:00 28.50
2023-09-20 13:00:00+03:00 28.32
2023-09-20 14:00:00+03:00 28.18
2023-09-20 15:00:00+03:00 27.90
2023-09-20 16:00:00+03:00 27.86
2023-09-20 17:00:00+03:00 27.26
2023-09-20 18:00:00+03:00 27.34
2023-09-21 09:00:00+03:00 27.30
2023-09-21 10:00:00+03:00 26.96
2023-09-21 11:00:00+03:00 27.20
2023-09-21 12:00:00+03:00 27.04
2023-09-21 13:00:00+03:00 27.20
2023-09-21 14:00:00+03:00 27.82
2023-09-21 15:00:00+03:00 27.72
2023-09-21 16:00:00+03:00 28.00
2023-09-21 17:00:00+03:00 28.46
2023-09-21 18:00:00+03:00 28.60
2023-09-22 09:00:00+03:00 28.78
2023-09-22 10:00:00+03:00 28.46
2023-09-22 11:00:00+03:00 28.44
2023-09-22 12:00:00+03:00 28.30
2023-09-22 13:00:00+03:00 28.26
2023-09-22 14:00:00+03:00 28.12
2023-09-22 15:00:00+03:00 28.04
2023-09-22 16:00:00+03:00 28.04
2023-09-22 17:00:00+03:00 27.94
2023-09-22 18:00:00+03:00 28.02
Mean of the first 10 values: price 28.638
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 28.22
2023-09-25 10:00:00+03:00 28.38
2023-09-25 11:00:00+03:00 28.54
2023-09-25 12:00:00+03:00 28.58
2023-09-25 13:00:00+03:00 28.60
2023-09-25 14:00:00+03:00 28.68
2023-09-25 15:00:00+03:00 28.54
2023-09-25 16:00:00+03:00 28.88
2023-09-25 17:00:00+03:00 28.96
2023-09-25 18:00:00+03:00 29.00
price
timestamp
2018-01-02 10:00:00+03:00 0.0204
2018-01-02 11:00:00+03:00 -0.0051
2018-01-02 12:00:00+03:00 -0.0034
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 0.0144
... ...
2023-09-22 14:00:00+03:00 -0.1400
2023-09-22 15:00:00+03:00 -0.0800
2023-09-22 16:00:00+03:00 0.0000
2023-09-22 17:00:00+03:00 -0.1000
2023-09-22 18:00:00+03:00 0.0800
[14226 rows x 1 columns]
ADF Statistic: -17.534053619974674
p-value: 4.222304233206124e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 9132.750
Date: Sun, 24 Dec 2023 AIC -18201.499
Time: 01:40:06 BIC -17959.489
Sample: 0 HQIC -18120.995
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.2621 0.125 -2.094 0.036 -0.508 -0.017
ar.L2 -0.0311 0.005 -6.617 0.000 -0.040 -0.022
ar.L3 -0.0376 0.006 -6.485 0.000 -0.049 -0.026
ar.L4 -0.0009 0.005 -0.194 0.847 -0.010 0.008
ar.L5 0.0149 0.003 4.271 0.000 0.008 0.022
ar.L6 0.0105 0.004 2.803 0.005 0.003 0.018
ar.L7 0.0735 0.003 22.574 0.000 0.067 0.080
ar.L8 0.0304 0.010 3.052 0.002 0.011 0.050
ar.L9 0.0473 0.003 13.623 0.000 0.040 0.054
ar.L10 -0.0059 0.006 -1.021 0.307 -0.017 0.005
ar.L11 -0.0405 0.004 -11.477 0.000 -0.047 -0.034
ar.L12 -0.0599 0.005 -10.969 0.000 -0.071 -0.049
ar.L13 -0.0226 0.007 -3.156 0.002 -0.037 -0.009
ar.L14 0.0265 0.004 6.707 0.000 0.019 0.034
ar.L15 0.0039 0.005 0.751 0.453 -0.006 0.014
ar.L16 0.0162 0.003 5.031 0.000 0.010 0.023
ar.L17 0.0030 0.004 0.751 0.453 -0.005 0.011
ar.L18 0.0103 0.004 2.558 0.011 0.002 0.018
ar.L19 0.0472 0.004 11.241 0.000 0.039 0.055
ar.L20 0.0452 0.006 7.165 0.000 0.033 0.058
ar.L21 -0.0332 0.005 -6.443 0.000 -0.043 -0.023
ar.L22 0.0006 0.006 0.091 0.928 -0.012 0.013
ar.L23 -0.0174 0.004 -4.551 0.000 -0.025 -0.010
ar.L24 0.0179 0.004 4.500 0.000 0.010 0.026
ar.L25 0.0121 0.005 2.538 0.011 0.003 0.021
ar.L26 -0.0311 0.003 -9.045 0.000 -0.038 -0.024
ar.L27 -0.0362 0.005 -6.816 0.000 -0.047 -0.026
ar.L28 -0.0546 0.005 -10.916 0.000 -0.064 -0.045
ar.L29 -0.0219 0.007 -3.271 0.001 -0.035 -0.009
ar.L30 0.0233 0.004 6.588 0.000 0.016 0.030
ma.L1 0.2824 0.125 2.262 0.024 0.038 0.527
sigma2 0.0162 4.26e-05 380.429 0.000 0.016 0.016
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1541041.44
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 156.40 Skew: 1.34
Prob(H) (two-sided): 0.00 Kurtosis: 53.92
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 27.948406
14228 27.974134
14229 27.972966
14230 28.009500
14231 28.099376
14232 28.104799
14233 28.136263
14234 28.177788
14235 28.176670
14236 28.201441
Name: predicted_mean, dtype: float64
lower price upper price
14227 27.698842 28.197971
14228 27.617608 28.330660
14229 27.540073 28.405859
14230 27.515311 28.503689
14231 27.549940 28.648813
14232 27.503644 28.705955
14233 27.486775 28.785751
14234 27.476909 28.878667
14235 27.426876 28.926465
14236 27.402382 29.000500
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.5808936652885004
arima_forecast('KOZAA', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 63.60
2023-09-20 10:00:00+03:00 65.40
2023-09-20 11:00:00+03:00 64.65
2023-09-20 12:00:00+03:00 66.45
2023-09-20 13:00:00+03:00 65.90
2023-09-20 14:00:00+03:00 65.65
2023-09-20 15:00:00+03:00 64.25
2023-09-20 16:00:00+03:00 64.30
2023-09-20 17:00:00+03:00 64.30
2023-09-20 18:00:00+03:00 64.10
2023-09-21 09:00:00+03:00 63.95
2023-09-21 10:00:00+03:00 63.25
2023-09-21 11:00:00+03:00 63.90
2023-09-21 12:00:00+03:00 63.65
2023-09-21 13:00:00+03:00 63.55
2023-09-21 14:00:00+03:00 65.25
2023-09-21 15:00:00+03:00 65.05
2023-09-21 16:00:00+03:00 65.85
2023-09-21 17:00:00+03:00 66.75
2023-09-21 18:00:00+03:00 66.65
2023-09-22 09:00:00+03:00 65.80
2023-09-22 10:00:00+03:00 65.90
2023-09-22 11:00:00+03:00 66.00
2023-09-22 12:00:00+03:00 65.40
2023-09-22 13:00:00+03:00 65.20
2023-09-22 14:00:00+03:00 64.60
2023-09-22 15:00:00+03:00 64.60
2023-09-22 16:00:00+03:00 64.45
2023-09-22 17:00:00+03:00 64.15
2023-09-22 18:00:00+03:00 64.45
Mean of the first 10 values: price 65.285
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 65.30
2023-09-25 10:00:00+03:00 65.15
2023-09-25 11:00:00+03:00 65.00
2023-09-25 12:00:00+03:00 65.20
2023-09-25 13:00:00+03:00 65.55
2023-09-25 14:00:00+03:00 65.60
2023-09-25 15:00:00+03:00 64.75
2023-09-25 16:00:00+03:00 65.25
2023-09-25 17:00:00+03:00 65.50
2023-09-25 18:00:00+03:00 65.55
price
timestamp
2018-01-02 10:00:00+03:00 0.02
2018-01-02 11:00:00+03:00 -0.03
2018-01-02 12:00:00+03:00 -0.02
2018-01-02 13:00:00+03:00 0.00
2018-01-02 14:00:00+03:00 0.02
... ...
2023-09-22 14:00:00+03:00 -0.60
2023-09-22 15:00:00+03:00 0.00
2023-09-22 16:00:00+03:00 -0.15
2023-09-22 17:00:00+03:00 -0.30
2023-09-22 18:00:00+03:00 0.30
[14226 rows x 1 columns]
ADF Statistic: -15.864295639873056
p-value: 9.135692280899592e-29
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood -2559.492
Date: Sun, 24 Dec 2023 AIC 5182.985
Time: 01:40:35 BIC 5424.995
Sample: 0 HQIC 5263.489
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0016 0.398 0.004 0.997 -0.778 0.781
ar.L2 0.0048 0.005 1.039 0.299 -0.004 0.014
ar.L3 -0.0289 0.004 -7.836 0.000 -0.036 -0.022
ar.L4 0.0048 0.012 0.417 0.676 -0.018 0.027
ar.L5 -0.0204 0.004 -5.038 0.000 -0.028 -0.012
ar.L6 0.0173 0.009 1.968 0.049 7.07e-05 0.034
ar.L7 0.0570 0.008 7.105 0.000 0.041 0.073
ar.L8 -0.0081 0.023 -0.348 0.728 -0.054 0.038
ar.L9 0.0282 0.005 5.857 0.000 0.019 0.038
ar.L10 -0.0118 0.012 -1.029 0.304 -0.034 0.011
ar.L11 -0.0368 0.005 -6.756 0.000 -0.048 -0.026
ar.L12 -0.0221 0.015 -1.468 0.142 -0.052 0.007
ar.L13 -0.0308 0.010 -3.219 0.001 -0.050 -0.012
ar.L14 0.0225 0.013 1.777 0.076 -0.002 0.047
ar.L15 0.0036 0.010 0.355 0.723 -0.016 0.024
ar.L16 0.0054 0.004 1.337 0.181 -0.003 0.013
ar.L17 -0.0099 0.005 -2.161 0.031 -0.019 -0.001
ar.L18 0.0139 0.006 2.262 0.024 0.002 0.026
ar.L19 0.0415 0.007 5.923 0.000 0.028 0.055
ar.L20 0.0484 0.017 2.901 0.004 0.016 0.081
ar.L21 -0.0161 0.019 -0.828 0.407 -0.054 0.022
ar.L22 0.0030 0.007 0.406 0.685 -0.012 0.018
ar.L23 -0.0068 0.004 -1.758 0.079 -0.014 0.001
ar.L24 0.0219 0.005 4.722 0.000 0.013 0.031
ar.L25 0.0149 0.010 1.458 0.145 -0.005 0.035
ar.L26 -0.0189 0.007 -2.647 0.008 -0.033 -0.005
ar.L27 0.0031 0.009 0.369 0.712 -0.014 0.020
ar.L28 -0.0297 0.004 -6.835 0.000 -0.038 -0.021
ar.L29 0.0377 0.013 2.992 0.003 0.013 0.062
ar.L30 0.0096 0.016 0.621 0.535 -0.021 0.040
ma.L1 0.0017 0.397 0.004 0.997 -0.777 0.780
sigma2 0.0839 0.000 326.465 0.000 0.083 0.084
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 687694.64
Prob(Q): 0.98 Prob(JB): 0.00
Heteroskedasticity (H): 53.01 Skew: 1.25
Prob(H) (two-sided): 0.00 Kurtosis: 36.97
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 64.454724
14228 64.392487
14229 64.493224
14230 64.522109
14231 64.578713
14232 64.590244
14233 64.655377
14234 64.764319
14235 64.770127
14236 64.773252
Name: predicted_mean, dtype: float64
lower price upper price
14227 63.887000 65.022447
14228 63.588302 65.196672
14229 63.506191 65.480257
14230 63.389307 65.654912
14231 63.315695 65.841732
14232 63.213929 65.966560
14233 63.170595 66.140158
14234 63.166898 66.361740
14235 63.068768 66.471486
14236 62.968811 66.577693
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.7313392743337747
arima_forecast('PGSUS', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 780.0
2023-09-20 10:00:00+03:00 778.3
2023-09-20 11:00:00+03:00 773.8
2023-09-20 12:00:00+03:00 774.6
2023-09-20 13:00:00+03:00 772.6
2023-09-20 14:00:00+03:00 773.9
2023-09-20 15:00:00+03:00 769.5
2023-09-20 16:00:00+03:00 760.7
2023-09-20 17:00:00+03:00 758.7
2023-09-20 18:00:00+03:00 755.0
2023-09-21 09:00:00+03:00 754.0
2023-09-21 10:00:00+03:00 742.9
2023-09-21 11:00:00+03:00 744.3
2023-09-21 12:00:00+03:00 742.4
2023-09-21 13:00:00+03:00 746.4
2023-09-21 14:00:00+03:00 764.3
2023-09-21 15:00:00+03:00 756.8
2023-09-21 16:00:00+03:00 763.7
2023-09-21 17:00:00+03:00 774.1
2023-09-21 18:00:00+03:00 772.8
2023-09-22 09:00:00+03:00 779.8
2023-09-22 10:00:00+03:00 769.5
2023-09-22 11:00:00+03:00 774.7
2023-09-22 12:00:00+03:00 773.2
2023-09-22 13:00:00+03:00 770.6
2023-09-22 14:00:00+03:00 762.5
2023-09-22 15:00:00+03:00 761.6
2023-09-22 16:00:00+03:00 761.4
2023-09-22 17:00:00+03:00 766.2
2023-09-22 18:00:00+03:00 767.2
Mean of the first 10 values: price 781.74
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 770.0
2023-09-25 10:00:00+03:00 763.0
2023-09-25 11:00:00+03:00 772.6
2023-09-25 12:00:00+03:00 781.6
2023-09-25 13:00:00+03:00 782.8
2023-09-25 14:00:00+03:00 778.9
2023-09-25 15:00:00+03:00 780.1
2023-09-25 16:00:00+03:00 787.4
2023-09-25 17:00:00+03:00 801.0
2023-09-25 18:00:00+03:00 800.0
price
timestamp
2018-01-02 10:00:00+03:00 0.30
2018-01-02 11:00:00+03:00 0.74
2018-01-02 12:00:00+03:00 0.08
2018-01-02 13:00:00+03:00 -0.02
2018-01-02 14:00:00+03:00 0.20
... ...
2023-09-22 14:00:00+03:00 -8.10
2023-09-22 15:00:00+03:00 -0.90
2023-09-22 16:00:00+03:00 -0.20
2023-09-22 17:00:00+03:00 4.80
2023-09-22 18:00:00+03:00 1.00
[14221 rows x 1 columns]
ADF Statistic: -17.44002637430114
p-value: 4.7149999043698234e-30
Critical Values: {'1%': -3.4308113121979202, '5%': -2.861743879167081, '10%': -2.5668785201103015}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14222
Model: ARIMA(30, 1, 1) Log Likelihood -32701.645
Date: Sun, 24 Dec 2023 AIC 65467.290
Time: 01:53:59 BIC 65709.289
Sample: 0 HQIC 65547.792
- 14222
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.4456 0.093 4.789 0.000 0.263 0.628
ar.L2 0.0023 0.006 0.399 0.690 -0.009 0.014
ar.L3 0.0205 0.004 5.066 0.000 0.013 0.028
ar.L4 0.0384 0.003 11.151 0.000 0.032 0.045
ar.L5 -0.0165 0.005 -3.224 0.001 -0.026 -0.006
ar.L6 0.0563 0.003 16.614 0.000 0.050 0.063
ar.L7 -0.0174 0.007 -2.564 0.010 -0.031 -0.004
ar.L8 -0.0256 0.004 -5.687 0.000 -0.034 -0.017
ar.L9 0.0266 0.005 5.824 0.000 0.018 0.036
ar.L10 0.0157 0.004 4.469 0.000 0.009 0.023
ar.L11 -0.0257 0.003 -7.710 0.000 -0.032 -0.019
ar.L12 -0.0241 0.004 -5.831 0.000 -0.032 -0.016
ar.L13 0.0198 0.005 3.864 0.000 0.010 0.030
ar.L14 0.0104 0.003 3.049 0.002 0.004 0.017
ar.L15 -0.0131 0.004 -3.701 0.000 -0.020 -0.006
ar.L16 0.0102 0.004 2.669 0.008 0.003 0.018
ar.L17 0.0083 0.004 2.187 0.029 0.001 0.016
ar.L18 -0.0257 0.004 -5.852 0.000 -0.034 -0.017
ar.L19 0.0143 0.004 3.700 0.000 0.007 0.022
ar.L20 -0.0275 0.003 -9.322 0.000 -0.033 -0.022
ar.L21 0.0017 0.004 0.416 0.677 -0.006 0.010
ar.L22 -0.0196 0.005 -4.288 0.000 -0.029 -0.011
ar.L23 -0.0042 0.005 -0.876 0.381 -0.014 0.005
ar.L24 -0.0088 0.004 -2.137 0.033 -0.017 -0.001
ar.L25 0.0255 0.004 5.894 0.000 0.017 0.034
ar.L26 -0.0038 0.004 -0.897 0.369 -0.012 0.004
ar.L27 0.0263 0.004 6.696 0.000 0.019 0.034
ar.L28 -0.0050 0.005 -1.019 0.308 -0.015 0.005
ar.L29 0.0148 0.003 4.286 0.000 0.008 0.022
ar.L30 0.0156 0.004 4.398 0.000 0.009 0.023
ma.L1 -0.4935 0.093 -5.325 0.000 -0.675 -0.312
sigma2 5.8193 0.013 447.311 0.000 5.794 5.845
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 3752783.47
Prob(Q): 0.97 Prob(JB): 0.00
Heteroskedasticity (H): 118.53 Skew: 2.51
Prob(H) (two-sided): 0.00 Kurtosis: 82.42
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14222 767.121007
14223 767.504803
14224 767.888411
14225 767.996863
14226 768.095480
14227 767.365248
14228 766.795915
14229 766.230573
14230 765.045697
14231 764.605613
Name: predicted_mean, dtype: float64
lower price upper price
14222 762.392948 771.849066
14223 760.976566 774.033039
14224 760.009435 775.767387
14225 758.939458 777.054269
14226 757.905314 778.285646
14227 756.155516 778.574980
14228 754.546075 779.045755
14229 753.010061 779.451085
14230 750.955968 779.135426
14231 749.666317 779.544908
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 19.37967373109998
arima_forecast('PETKM', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 18.74
2023-09-20 10:00:00+03:00 18.87
2023-09-20 11:00:00+03:00 18.68
2023-09-20 12:00:00+03:00 18.68
2023-09-20 13:00:00+03:00 18.70
2023-09-20 14:00:00+03:00 18.67
2023-09-20 15:00:00+03:00 19.35
2023-09-20 16:00:00+03:00 19.25
2023-09-20 17:00:00+03:00 19.03
2023-09-20 18:00:00+03:00 18.92
2023-09-21 09:00:00+03:00 18.90
2023-09-21 10:00:00+03:00 18.62
2023-09-21 11:00:00+03:00 18.70
2023-09-21 12:00:00+03:00 18.65
2023-09-21 13:00:00+03:00 18.74
2023-09-21 14:00:00+03:00 19.18
2023-09-21 15:00:00+03:00 19.14
2023-09-21 16:00:00+03:00 19.43
2023-09-21 17:00:00+03:00 19.69
2023-09-21 18:00:00+03:00 19.69
2023-09-22 09:00:00+03:00 19.70
2023-09-22 10:00:00+03:00 20.08
2023-09-22 11:00:00+03:00 20.16
2023-09-22 12:00:00+03:00 20.04
2023-09-22 13:00:00+03:00 19.91
2023-09-22 14:00:00+03:00 19.75
2023-09-22 15:00:00+03:00 19.77
2023-09-22 16:00:00+03:00 19.80
2023-09-22 17:00:00+03:00 19.95
2023-09-22 18:00:00+03:00 19.90
Mean of the first 10 values: price 20.306
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 20.10
2023-09-25 10:00:00+03:00 20.22
2023-09-25 11:00:00+03:00 20.26
2023-09-25 12:00:00+03:00 20.26
2023-09-25 13:00:00+03:00 20.40
2023-09-25 14:00:00+03:00 20.30
2023-09-25 15:00:00+03:00 20.34
2023-09-25 16:00:00+03:00 20.40
2023-09-25 17:00:00+03:00 20.40
2023-09-25 18:00:00+03:00 20.38
price
timestamp
2018-01-02 10:00:00+03:00 0.0338
2018-01-02 11:00:00+03:00 0.0112
2018-01-02 12:00:00+03:00 0.0056
2018-01-02 13:00:00+03:00 0.0056
2018-01-02 14:00:00+03:00 0.0282
... ...
2023-09-22 14:00:00+03:00 -0.1600
2023-09-22 15:00:00+03:00 0.0200
2023-09-22 16:00:00+03:00 0.0300
2023-09-22 17:00:00+03:00 0.1500
2023-09-22 18:00:00+03:00 -0.0500
[14226 rows x 1 columns]
ADF Statistic: -16.614029542524218
p-value: 1.720907954011102e-29
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 16627.960
Date: Sun, 24 Dec 2023 AIC -33191.921
Time: 01:56:15 BIC -32949.910
Sample: 0 HQIC -33111.416
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.4649 0.172 2.696 0.007 0.127 0.803
ar.L2 -0.0425 0.012 -3.563 0.000 -0.066 -0.019
ar.L3 0.0339 0.005 6.543 0.000 0.024 0.044
ar.L4 -0.0011 0.006 -0.179 0.858 -0.014 0.011
ar.L5 0.0092 0.005 2.033 0.042 0.000 0.018
ar.L6 0.0250 0.005 5.111 0.000 0.015 0.035
ar.L7 -0.0262 0.007 -3.567 0.000 -0.041 -0.012
ar.L8 -0.0184 0.005 -3.402 0.001 -0.029 -0.008
ar.L9 0.0606 0.006 9.998 0.000 0.049 0.073
ar.L10 -0.0591 0.009 -6.236 0.000 -0.078 -0.041
ar.L11 0.0094 0.008 1.208 0.227 -0.006 0.025
ar.L12 -0.0506 0.005 -9.936 0.000 -0.061 -0.041
ar.L13 0.0177 0.010 1.692 0.091 -0.003 0.038
ar.L14 0.0020 0.005 0.416 0.678 -0.007 0.011
ar.L15 -0.0181 0.005 -3.807 0.000 -0.027 -0.009
ar.L16 0.0386 0.006 6.879 0.000 0.028 0.050
ar.L17 0.0003 0.007 0.035 0.972 -0.014 0.014
ar.L18 -0.0104 0.006 -1.816 0.069 -0.022 0.001
ar.L19 0.0025 0.005 0.476 0.634 -0.008 0.013
ar.L20 0.0483 0.004 10.769 0.000 0.040 0.057
ar.L21 -0.0144 0.009 -1.533 0.125 -0.033 0.004
ar.L22 -0.0037 0.005 -0.724 0.469 -0.014 0.006
ar.L23 -0.0160 0.005 -3.190 0.001 -0.026 -0.006
ar.L24 0.0081 0.006 1.353 0.176 -0.004 0.020
ar.L25 -0.0188 0.005 -3.773 0.000 -0.029 -0.009
ar.L26 0.0065 0.006 1.084 0.278 -0.005 0.018
ar.L27 -0.0170 0.005 -3.303 0.001 -0.027 -0.007
ar.L28 -0.0088 0.006 -1.424 0.155 -0.021 0.003
ar.L29 0.0204 0.006 3.601 0.000 0.009 0.032
ar.L30 0.0135 0.006 2.276 0.023 0.002 0.025
ma.L1 -0.3976 0.172 -2.306 0.021 -0.736 -0.060
sigma2 0.0057 2e-05 282.997 0.000 0.006 0.006
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 365307.73
Prob(Q): 0.97 Prob(JB): 0.00
Heteroskedasticity (H): 26.12 Skew: 0.08
Prob(H) (two-sided): 0.00 Kurtosis: 27.82
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 19.895279
14228 19.882561
14229 19.878222
14230 19.853798
14231 19.860673
14232 19.907857
14233 19.931177
14234 19.971163
14235 19.983371
14236 19.980858
Name: predicted_mean, dtype: float64
lower price upper price
14227 19.747928 20.042630
14228 19.667056 20.098065
14229 19.612414 20.144030
14230 19.543858 20.163739
14231 19.511223 20.210123
14232 19.522063 20.293650
14233 19.510294 20.352060
14234 19.518428 20.423897
14235 19.502179 20.464563
14236 19.470388 20.491328
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.3993884935461538
arima_forecast('SAHOL', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 56.05
2023-09-20 10:00:00+03:00 56.70
2023-09-20 11:00:00+03:00 57.10
2023-09-20 12:00:00+03:00 57.05
2023-09-20 13:00:00+03:00 56.85
2023-09-20 14:00:00+03:00 56.80
2023-09-20 15:00:00+03:00 56.85
2023-09-20 16:00:00+03:00 56.75
2023-09-20 17:00:00+03:00 55.75
2023-09-20 18:00:00+03:00 55.55
2023-09-21 09:00:00+03:00 55.45
2023-09-21 10:00:00+03:00 56.15
2023-09-21 11:00:00+03:00 55.70
2023-09-21 12:00:00+03:00 55.60
2023-09-21 13:00:00+03:00 55.75
2023-09-21 14:00:00+03:00 56.45
2023-09-21 15:00:00+03:00 56.45
2023-09-21 16:00:00+03:00 56.60
2023-09-21 17:00:00+03:00 56.90
2023-09-21 18:00:00+03:00 56.95
2023-09-22 09:00:00+03:00 57.40
2023-09-22 10:00:00+03:00 57.05
2023-09-22 11:00:00+03:00 56.85
2023-09-22 12:00:00+03:00 56.95
2023-09-22 13:00:00+03:00 56.95
2023-09-22 14:00:00+03:00 56.50
2023-09-22 15:00:00+03:00 56.50
2023-09-22 16:00:00+03:00 56.70
2023-09-22 17:00:00+03:00 56.40
2023-09-22 18:00:00+03:00 56.50
Mean of the first 10 values: price 57.66
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 56.75
2023-09-25 10:00:00+03:00 57.40
2023-09-25 11:00:00+03:00 57.45
2023-09-25 12:00:00+03:00 57.75
2023-09-25 13:00:00+03:00 57.90
2023-09-25 14:00:00+03:00 57.75
2023-09-25 15:00:00+03:00 57.60
2023-09-25 16:00:00+03:00 57.95
2023-09-25 17:00:00+03:00 57.95
2023-09-25 18:00:00+03:00 58.10
price
timestamp
2018-01-02 10:00:00+03:00 0.0000
2018-01-02 11:00:00+03:00 0.0630
2018-01-02 12:00:00+03:00 -0.0315
2018-01-02 13:00:00+03:00 0.0236
2018-01-02 14:00:00+03:00 0.0157
... ...
2023-09-22 14:00:00+03:00 -0.4500
2023-09-22 15:00:00+03:00 0.0000
2023-09-22 16:00:00+03:00 0.2000
2023-09-22 17:00:00+03:00 -0.3000
2023-09-22 18:00:00+03:00 0.1000
[14225 rows x 1 columns]
ADF Statistic: -22.28408897909576
p-value: 0.0
Critical Values: {'1%': -3.4308106947070907, '5%': -2.8617436062851787, '10%': -2.566878374857977}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14226
Model: ARIMA(30, 1, 1) Log Likelihood 4906.693
Date: Sun, 24 Dec 2023 AIC -9749.386
Time: 01:56:47 BIC -9507.378
Sample: 0 HQIC -9668.882
- 14226
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.0155 0.934 -0.017 0.987 -1.846 1.815
ar.L2 0.0160 0.029 0.557 0.577 -0.040 0.072
ar.L3 0.0163 0.015 1.115 0.265 -0.012 0.045
ar.L4 -0.0052 0.015 -0.335 0.738 -0.035 0.025
ar.L5 0.0105 0.006 1.772 0.076 -0.001 0.022
ar.L6 0.0311 0.010 2.995 0.003 0.011 0.051
ar.L7 -0.0018 0.029 -0.060 0.952 -0.059 0.056
ar.L8 -0.0070 0.004 -1.663 0.096 -0.015 0.001
ar.L9 0.0250 0.007 3.343 0.001 0.010 0.040
ar.L10 -0.0345 0.024 -1.450 0.147 -0.081 0.012
ar.L11 -0.0265 0.032 -0.829 0.407 -0.089 0.036
ar.L12 -0.0299 0.026 -1.170 0.242 -0.080 0.020
ar.L13 0.0110 0.029 0.382 0.703 -0.045 0.067
ar.L14 0.0062 0.010 0.602 0.547 -0.014 0.026
ar.L15 -0.0326 0.007 -4.780 0.000 -0.046 -0.019
ar.L16 0.0174 0.031 0.568 0.570 -0.043 0.078
ar.L17 0.0215 0.016 1.318 0.188 -0.010 0.054
ar.L18 -0.0144 0.021 -0.695 0.487 -0.055 0.026
ar.L19 -0.0124 0.013 -0.923 0.356 -0.039 0.014
ar.L20 0.0225 0.012 1.860 0.063 -0.001 0.046
ar.L21 -0.0273 0.022 -1.265 0.206 -0.070 0.015
ar.L22 0.0090 0.026 0.349 0.727 -0.042 0.060
ar.L23 -0.0161 0.009 -1.754 0.079 -0.034 0.002
ar.L24 0.0038 0.015 0.251 0.802 -0.026 0.034
ar.L25 -0.0053 0.005 -1.032 0.302 -0.015 0.005
ar.L26 0.0050 0.006 0.811 0.418 -0.007 0.017
ar.L27 0.0225 0.006 3.687 0.000 0.011 0.034
ar.L28 0.0253 0.021 1.176 0.240 -0.017 0.067
ar.L29 0.0059 0.024 0.245 0.807 -0.042 0.054
ar.L30 -0.0044 0.007 -0.634 0.526 -0.018 0.009
ma.L1 -0.0152 0.934 -0.016 0.987 -1.846 1.815
sigma2 0.0294 8.72e-05 336.883 0.000 0.029 0.030
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1320701.75
Prob(Q): 0.96 Prob(JB): 0.00
Heteroskedasticity (H): 38.71 Skew: -0.20
Prob(H) (two-sided): 0.00 Kurtosis: 50.20
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14226 56.442763
14227 56.470511
14228 56.453891
14229 56.478718
14230 56.445484
14231 56.441977
14232 56.420716
14233 56.425717
14234 56.413471
14235 56.408408
Name: predicted_mean, dtype: float64
lower price upper price
14226 56.106872 56.778655
14227 56.002729 56.938294
14228 55.880779 57.027002
14229 55.814196 57.143239
14230 55.701541 57.189427
14231 55.624743 57.259211
14232 55.532249 57.309183
14233 55.471688 57.379745
14234 55.398740 57.428201
14235 55.333548 57.483267
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 1.2778824501907873
arima_forecast('SASA', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 46.72
2023-09-20 10:00:00+03:00 46.76
2023-09-20 11:00:00+03:00 46.48
2023-09-20 12:00:00+03:00 46.30
2023-09-20 13:00:00+03:00 46.04
2023-09-20 14:00:00+03:00 45.80
2023-09-20 15:00:00+03:00 45.76
2023-09-20 16:00:00+03:00 46.18
2023-09-20 17:00:00+03:00 45.86
2023-09-20 18:00:00+03:00 45.60
2023-09-21 09:00:00+03:00 45.60
2023-09-21 10:00:00+03:00 44.78
2023-09-21 11:00:00+03:00 44.86
2023-09-21 12:00:00+03:00 44.68
2023-09-21 13:00:00+03:00 44.52
2023-09-21 14:00:00+03:00 45.64
2023-09-21 15:00:00+03:00 45.52
2023-09-21 16:00:00+03:00 45.78
2023-09-21 17:00:00+03:00 46.26
2023-09-21 18:00:00+03:00 46.30
2023-09-22 09:00:00+03:00 46.30
2023-09-22 10:00:00+03:00 46.34
2023-09-22 11:00:00+03:00 46.44
2023-09-22 12:00:00+03:00 46.20
2023-09-22 13:00:00+03:00 45.92
2023-09-22 14:00:00+03:00 45.68
2023-09-22 15:00:00+03:00 45.54
2023-09-22 16:00:00+03:00 45.56
2023-09-22 17:00:00+03:00 45.52
2023-09-22 18:00:00+03:00 45.52
Mean of the first 10 values: price 45.648
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 45.60
2023-09-25 10:00:00+03:00 45.28
2023-09-25 11:00:00+03:00 45.54
2023-09-25 12:00:00+03:00 45.66
2023-09-25 13:00:00+03:00 45.54
2023-09-25 14:00:00+03:00 45.46
2023-09-25 15:00:00+03:00 45.66
2023-09-25 16:00:00+03:00 45.92
2023-09-25 17:00:00+03:00 45.92
2023-09-25 18:00:00+03:00 45.90
price
timestamp
2018-01-02 10:00:00+03:00 0.0069
2018-01-02 11:00:00+03:00 0.0277
2018-01-02 12:00:00+03:00 0.0193
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 0.0000
... ...
2023-09-22 14:00:00+03:00 -0.2400
2023-09-22 15:00:00+03:00 -0.1400
2023-09-22 16:00:00+03:00 0.0200
2023-09-22 17:00:00+03:00 -0.0400
2023-09-22 18:00:00+03:00 0.0000
[14224 rows x 1 columns]
ADF Statistic: -17.222504680246793
p-value: 6.2667834968518094e-30
Critical Values: {'1%': -3.430811214589344, '5%': -2.8617438360318466, '10%': -2.5668784971498444}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14225
Model: ARIMA(30, 1, 1) Log Likelihood -3793.035
Date: Sun, 24 Dec 2023 AIC 7650.070
Time: 01:57:43 BIC 7892.076
Sample: 0 HQIC 7730.574
- 14225
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.2169 0.039 -5.577 0.000 -0.293 -0.141
ar.L2 -0.0484 0.004 -12.147 0.000 -0.056 -0.041
ar.L3 -0.0327 0.003 -9.394 0.000 -0.040 -0.026
ar.L4 -0.0014 0.004 -0.329 0.742 -0.010 0.007
ar.L5 0.0255 0.004 6.238 0.000 0.018 0.034
ar.L6 0.0449 0.004 10.302 0.000 0.036 0.053
ar.L7 0.0358 0.004 8.756 0.000 0.028 0.044
ar.L8 -0.0067 0.004 -1.657 0.098 -0.015 0.001
ar.L9 0.1274 0.002 59.183 0.000 0.123 0.132
ar.L10 0.1736 0.005 33.653 0.000 0.163 0.184
ar.L11 -0.0280 0.006 -4.454 0.000 -0.040 -0.016
ar.L12 -0.0385 0.004 -9.166 0.000 -0.047 -0.030
ar.L13 -0.0547 0.003 -16.076 0.000 -0.061 -0.048
ar.L14 0.0138 0.004 3.442 0.001 0.006 0.022
ar.L15 -0.0591 0.004 -14.645 0.000 -0.067 -0.051
ar.L16 -0.0264 0.004 -6.168 0.000 -0.035 -0.018
ar.L17 0.0246 0.003 7.383 0.000 0.018 0.031
ar.L18 -0.0178 0.005 -3.796 0.000 -0.027 -0.009
ar.L19 0.0131 0.002 5.609 0.000 0.009 0.018
ar.L20 -0.0233 0.003 -7.221 0.000 -0.030 -0.017
ar.L21 -0.0015 0.003 -0.465 0.642 -0.008 0.005
ar.L22 0.0064 0.004 1.664 0.096 -0.001 0.014
ar.L23 -0.0247 0.004 -6.288 0.000 -0.032 -0.017
ar.L24 0.0031 0.004 0.766 0.444 -0.005 0.011
ar.L25 0.0213 0.004 5.495 0.000 0.014 0.029
ar.L26 -0.0304 0.003 -8.910 0.000 -0.037 -0.024
ar.L27 -0.0192 0.004 -4.618 0.000 -0.027 -0.011
ar.L28 -0.0206 0.004 -5.735 0.000 -0.028 -0.014
ar.L29 0.0151 0.002 6.125 0.000 0.010 0.020
ar.L30 -0.0635 0.003 -23.272 0.000 -0.069 -0.058
ma.L1 0.1703 0.039 4.370 0.000 0.094 0.247
sigma2 0.0998 0.000 426.386 0.000 0.099 0.100
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 12705513.90
Prob(Q): 0.98 Prob(JB): 0.00
Heteroskedasticity (H): 3054.72 Skew: -0.37
Prob(H) (two-sided): 0.00 Kurtosis: 149.41
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14225 45.376930
14226 45.394853
14227 45.431346
14228 45.274084
14229 45.270161
14230 45.217859
14231 45.240339
14232 45.262325
14233 45.262988
14234 45.306661
Name: predicted_mean, dtype: float64
lower price upper price
14225 44.757770 45.996091
14226 44.539417 46.250290
14227 44.405303 46.457389
14228 44.108585 46.439582
14229 43.978396 46.561926
14230 43.804550 46.631167
14231 43.706049 46.774630
14232 43.610713 46.913936
14233 43.505771 47.020204
14234 43.422938 47.190384
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.4180313755090521
arima_forecast('SISE', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 52.35
2023-09-20 10:00:00+03:00 52.45
2023-09-20 11:00:00+03:00 52.05
2023-09-20 12:00:00+03:00 52.25
2023-09-20 13:00:00+03:00 52.10
2023-09-20 14:00:00+03:00 52.00
2023-09-20 15:00:00+03:00 51.75
2023-09-20 16:00:00+03:00 51.60
2023-09-20 17:00:00+03:00 51.05
2023-09-20 18:00:00+03:00 51.10
2023-09-21 09:00:00+03:00 50.60
2023-09-21 10:00:00+03:00 50.50
2023-09-21 11:00:00+03:00 50.70
2023-09-21 12:00:00+03:00 50.70
2023-09-21 13:00:00+03:00 51.25
2023-09-21 14:00:00+03:00 52.40
2023-09-21 15:00:00+03:00 52.20
2023-09-21 16:00:00+03:00 52.45
2023-09-21 17:00:00+03:00 53.15
2023-09-21 18:00:00+03:00 52.95
2023-09-22 09:00:00+03:00 53.75
2023-09-22 10:00:00+03:00 53.65
2023-09-22 11:00:00+03:00 53.55
2023-09-22 12:00:00+03:00 53.55
2023-09-22 13:00:00+03:00 53.50
2023-09-22 14:00:00+03:00 52.90
2023-09-22 15:00:00+03:00 52.75
2023-09-22 16:00:00+03:00 53.30
2023-09-22 17:00:00+03:00 54.70
2023-09-22 18:00:00+03:00 54.70
Mean of the first 10 values: price 55.305
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 54.80
2023-09-25 10:00:00+03:00 55.40
2023-09-25 11:00:00+03:00 55.55
2023-09-25 12:00:00+03:00 55.40
2023-09-25 13:00:00+03:00 55.45
2023-09-25 14:00:00+03:00 55.15
2023-09-25 15:00:00+03:00 55.30
2023-09-25 16:00:00+03:00 55.55
2023-09-25 17:00:00+03:00 55.25
2023-09-25 18:00:00+03:00 55.20
price
timestamp
2018-01-02 10:00:00+03:00 -0.0513
2018-01-02 11:00:00+03:00 0.0256
2018-01-02 12:00:00+03:00 0.0000
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 0.0257
... ...
2023-09-22 14:00:00+03:00 -0.6000
2023-09-22 15:00:00+03:00 -0.1500
2023-09-22 16:00:00+03:00 0.5500
2023-09-22 17:00:00+03:00 1.4000
2023-09-22 18:00:00+03:00 0.0000
[14226 rows x 1 columns]
ADF Statistic: -18.232040911642947
p-value: 2.3659845880078223e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 5099.806
Date: Sun, 24 Dec 2023 AIC -10135.611
Time: 01:59:20 BIC -9893.601
Sample: 0 HQIC -10055.107
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.5683 0.093 6.087 0.000 0.385 0.751
ar.L2 -0.0315 0.005 -6.748 0.000 -0.041 -0.022
ar.L3 0.0277 0.005 5.800 0.000 0.018 0.037
ar.L4 -0.0127 0.005 -2.644 0.008 -0.022 -0.003
ar.L5 0.0146 0.004 3.528 0.000 0.007 0.023
ar.L6 0.0350 0.005 7.396 0.000 0.026 0.044
ar.L7 -0.0069 0.006 -1.144 0.252 -0.019 0.005
ar.L8 -0.0245 0.005 -5.404 0.000 -0.033 -0.016
ar.L9 0.0342 0.005 7.521 0.000 0.025 0.043
ar.L10 -0.0322 0.005 -6.449 0.000 -0.042 -0.022
ar.L11 -0.0311 0.005 -6.911 0.000 -0.040 -0.022
ar.L12 -0.0103 0.006 -1.724 0.085 -0.022 0.001
ar.L13 0.0206 0.005 3.777 0.000 0.010 0.031
ar.L14 0.0021 0.004 0.464 0.643 -0.007 0.011
ar.L15 -0.0327 0.004 -7.347 0.000 -0.041 -0.024
ar.L16 0.0037 0.005 0.716 0.474 -0.006 0.014
ar.L17 0.0247 0.005 4.804 0.000 0.015 0.035
ar.L18 -0.0241 0.005 -4.599 0.000 -0.034 -0.014
ar.L19 0.0156 0.005 3.182 0.001 0.006 0.025
ar.L20 -0.0052 0.004 -1.299 0.194 -0.013 0.003
ar.L21 0.0034 0.004 0.967 0.334 -0.004 0.010
ar.L22 0.0166 0.004 4.038 0.000 0.009 0.025
ar.L23 -0.0041 0.005 -0.864 0.388 -0.014 0.005
ar.L24 -0.0361 0.005 -7.121 0.000 -0.046 -0.026
ar.L25 0.0228 0.005 4.189 0.000 0.012 0.033
ar.L26 -0.0352 0.004 -8.541 0.000 -0.043 -0.027
ar.L27 0.0142 0.006 2.545 0.011 0.003 0.025
ar.L28 0.0237 0.005 4.774 0.000 0.014 0.033
ar.L29 0.0092 0.006 1.661 0.097 -0.002 0.020
ar.L30 0.0136 0.005 2.659 0.008 0.004 0.024
ma.L1 -0.5502 0.093 -5.910 0.000 -0.733 -0.368
sigma2 0.0286 8.31e-05 343.948 0.000 0.028 0.029
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 2013750.05
Prob(Q): 0.93 Prob(JB): 0.00
Heteroskedasticity (H): 69.39 Skew: 1.36
Prob(H) (two-sided): 0.00 Kurtosis: 61.22
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 54.596165
14228 54.570830
14229 54.558428
14230 54.564688
14231 54.666201
14232 54.664194
14233 54.658052
14234 54.745348
14235 54.639738
14236 54.512242
Name: predicted_mean, dtype: float64
lower price upper price
14227 54.264800 54.927530
14228 54.097936 55.043725
14229 53.981560 55.135296
14230 53.897420 55.231957
14231 53.919844 55.412557
14232 53.844778 55.483610
14233 53.766096 55.550008
14234 53.784243 55.706453
14235 53.616025 55.663452
14236 53.426746 55.597738
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.7187052462466499
arima_forecast('TAVHL', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 115.1
2023-09-20 10:00:00+03:00 115.1
2023-09-20 11:00:00+03:00 116.4
2023-09-20 12:00:00+03:00 117.6
2023-09-20 13:00:00+03:00 118.4
2023-09-20 14:00:00+03:00 118.8
2023-09-20 15:00:00+03:00 119.9
2023-09-20 16:00:00+03:00 119.7
2023-09-20 17:00:00+03:00 119.9
2023-09-20 18:00:00+03:00 120.0
2023-09-21 09:00:00+03:00 119.2
2023-09-21 10:00:00+03:00 116.6
2023-09-21 11:00:00+03:00 117.9
2023-09-21 12:00:00+03:00 117.3
2023-09-21 13:00:00+03:00 117.7
2023-09-21 14:00:00+03:00 121.9
2023-09-21 15:00:00+03:00 120.6
2023-09-21 16:00:00+03:00 120.7
2023-09-21 17:00:00+03:00 121.9
2023-09-21 18:00:00+03:00 122.9
2023-09-22 09:00:00+03:00 119.5
2023-09-22 10:00:00+03:00 119.0
2023-09-22 11:00:00+03:00 119.3
2023-09-22 12:00:00+03:00 119.2
2023-09-22 13:00:00+03:00 119.4
2023-09-22 14:00:00+03:00 118.7
2023-09-22 15:00:00+03:00 118.7
2023-09-22 16:00:00+03:00 118.8
2023-09-22 17:00:00+03:00 119.2
2023-09-22 18:00:00+03:00 119.2
Mean of the first 10 values: price 121.53
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 119.3
2023-09-25 10:00:00+03:00 118.9
2023-09-25 11:00:00+03:00 121.1
2023-09-25 12:00:00+03:00 121.1
2023-09-25 13:00:00+03:00 122.7
2023-09-25 14:00:00+03:00 121.9
2023-09-25 15:00:00+03:00 121.8
2023-09-25 16:00:00+03:00 121.9
2023-09-25 17:00:00+03:00 123.5
2023-09-25 18:00:00+03:00 123.1
price
timestamp
2018-01-02 10:00:00+03:00 0.1635
2018-01-02 11:00:00+03:00 -0.0654
2018-01-02 12:00:00+03:00 0.0490
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 -0.0164
... ...
2023-09-22 14:00:00+03:00 -0.7000
2023-09-22 15:00:00+03:00 0.0000
2023-09-22 16:00:00+03:00 0.1000
2023-09-22 17:00:00+03:00 0.4000
2023-09-22 18:00:00+03:00 0.0000
[14226 rows x 1 columns]
ADF Statistic: -17.32985882953613
p-value: 5.418190371801927e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood -7661.180
Date: Sun, 24 Dec 2023 AIC 15386.360
Time: 02:00:49 BIC 15628.371
Sample: 0 HQIC 15466.865
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.7579 0.078 9.756 0.000 0.606 0.910
ar.L2 -0.0122 0.005 -2.425 0.015 -0.022 -0.002
ar.L3 0.0222 0.005 4.086 0.000 0.012 0.033
ar.L4 0.0053 0.006 0.947 0.344 -0.006 0.016
ar.L5 -0.0163 0.005 -3.023 0.002 -0.027 -0.006
ar.L6 0.0250 0.006 4.509 0.000 0.014 0.036
ar.L7 0.0238 0.006 4.238 0.000 0.013 0.035
ar.L8 -0.0495 0.006 -7.632 0.000 -0.062 -0.037
ar.L9 0.0474 0.006 8.192 0.000 0.036 0.059
ar.L10 -0.0197 0.005 -3.655 0.000 -0.030 -0.009
ar.L11 -0.0119 0.005 -2.514 0.012 -0.021 -0.003
ar.L12 -0.0060 0.006 -0.959 0.338 -0.018 0.006
ar.L13 -0.0040 0.006 -0.698 0.485 -0.015 0.007
ar.L14 0.0040 0.005 0.743 0.457 -0.007 0.015
ar.L15 0.0246 0.005 4.640 0.000 0.014 0.035
ar.L16 -0.0176 0.006 -3.067 0.002 -0.029 -0.006
ar.L17 0.0107 0.005 1.970 0.049 5.68e-05 0.021
ar.L18 -0.0150 0.006 -2.540 0.011 -0.027 -0.003
ar.L19 -0.0078 0.006 -1.286 0.198 -0.020 0.004
ar.L20 0.0345 0.006 6.246 0.000 0.024 0.045
ar.L21 -0.0098 0.006 -1.705 0.088 -0.021 0.001
ar.L22 -0.0183 0.006 -2.963 0.003 -0.030 -0.006
ar.L23 -0.0250 0.006 -4.159 0.000 -0.037 -0.013
ar.L24 0.0099 0.006 1.526 0.127 -0.003 0.023
ar.L25 0.0207 0.006 3.395 0.001 0.009 0.033
ar.L26 0.0009 0.006 0.152 0.879 -0.010 0.012
ar.L27 -0.0037 0.006 -0.602 0.547 -0.016 0.008
ar.L28 -0.0013 0.006 -0.214 0.830 -0.013 0.011
ar.L29 0.0094 0.006 1.590 0.112 -0.002 0.021
ar.L30 0.0086 0.005 1.598 0.110 -0.002 0.019
ma.L1 -0.7622 0.077 -9.851 0.000 -0.914 -0.611
sigma2 0.1719 0.001 292.717 0.000 0.171 0.173
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 380223.27
Prob(Q): 0.97 Prob(JB): 0.00
Heteroskedasticity (H): 12.48 Skew: 0.39
Prob(H) (two-sided): 0.00 Kurtosis: 28.32
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 119.326008
14228 119.314754
14229 119.465235
14230 119.641441
14231 119.792918
14232 119.930781
14233 119.961276
14234 119.913602
14235 119.865056
14236 119.972809
Name: predicted_mean, dtype: float64
lower price upper price
14227 118.513395 120.138621
14228 118.168017 120.461491
14229 118.068985 120.861486
14230 118.029705 121.253177
14231 117.986415 121.599420
14232 117.950798 121.910764
14233 117.815633 122.106919
14234 117.602454 122.224751
14235 117.405249 122.324864
14236 117.364387 122.581231
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 2.182208058147649
arima_forecast('TKFEN', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 49.16
2023-09-20 10:00:00+03:00 49.82
2023-09-20 11:00:00+03:00 49.36
2023-09-20 12:00:00+03:00 49.24
2023-09-20 13:00:00+03:00 48.66
2023-09-20 14:00:00+03:00 48.38
2023-09-20 15:00:00+03:00 48.12
2023-09-20 16:00:00+03:00 47.78
2023-09-20 17:00:00+03:00 47.78
2023-09-20 18:00:00+03:00 47.88
2023-09-21 09:00:00+03:00 47.58
2023-09-21 10:00:00+03:00 47.38
2023-09-21 11:00:00+03:00 47.92
2023-09-21 12:00:00+03:00 47.58
2023-09-21 13:00:00+03:00 47.88
2023-09-21 14:00:00+03:00 48.92
2023-09-21 15:00:00+03:00 49.08
2023-09-21 16:00:00+03:00 50.45
2023-09-21 17:00:00+03:00 50.95
2023-09-21 18:00:00+03:00 50.95
2023-09-22 09:00:00+03:00 53.80
2023-09-22 10:00:00+03:00 52.55
2023-09-22 11:00:00+03:00 52.35
2023-09-22 12:00:00+03:00 52.30
2023-09-22 13:00:00+03:00 52.55
2023-09-22 14:00:00+03:00 52.30
2023-09-22 15:00:00+03:00 52.20
2023-09-22 16:00:00+03:00 51.80
2023-09-22 17:00:00+03:00 52.00
2023-09-22 18:00:00+03:00 52.00
Mean of the first 10 values: price 52.09
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 52.20
2023-09-25 10:00:00+03:00 52.05
2023-09-25 11:00:00+03:00 51.90
2023-09-25 12:00:00+03:00 52.30
2023-09-25 13:00:00+03:00 52.25
2023-09-25 14:00:00+03:00 51.85
2023-09-25 15:00:00+03:00 52.00
2023-09-25 16:00:00+03:00 52.00
2023-09-25 17:00:00+03:00 52.05
2023-09-25 18:00:00+03:00 52.30
price
timestamp
2018-01-02 10:00:00+03:00 0.0600
2018-01-02 11:00:00+03:00 -0.0974
2018-01-02 12:00:00+03:00 -0.1123
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 0.0075
... ...
2023-09-22 14:00:00+03:00 -0.2500
2023-09-22 15:00:00+03:00 -0.1000
2023-09-22 16:00:00+03:00 -0.4000
2023-09-22 17:00:00+03:00 0.2000
2023-09-22 18:00:00+03:00 0.0000
[14226 rows x 1 columns]
ADF Statistic: -17.0398452406441
p-value: 8.211241012274152e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 1919.611
Date: Sun, 24 Dec 2023 AIC -3775.222
Time: 02:01:21 BIC -3533.211
Sample: 0 HQIC -3694.717
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0091 0.642 0.014 0.989 -1.250 1.268
ar.L2 0.0051 0.013 0.395 0.693 -0.020 0.030
ar.L3 0.0089 0.006 1.573 0.116 -0.002 0.020
ar.L4 0.0171 0.007 2.459 0.014 0.003 0.031
ar.L5 -0.0110 0.012 -0.914 0.361 -0.035 0.013
ar.L6 0.0046 0.008 0.565 0.572 -0.011 0.021
ar.L7 0.0124 0.006 2.208 0.027 0.001 0.023
ar.L8 -0.0058 0.009 -0.634 0.526 -0.024 0.012
ar.L9 0.0092 0.006 1.625 0.104 -0.002 0.020
ar.L10 0.0060 0.007 0.826 0.409 -0.008 0.020
ar.L11 -0.0330 0.006 -5.345 0.000 -0.045 -0.021
ar.L12 -0.0196 0.022 -0.878 0.380 -0.063 0.024
ar.L13 -0.0105 0.013 -0.792 0.429 -0.036 0.015
ar.L14 0.0099 0.008 1.233 0.218 -0.006 0.026
ar.L15 -0.0073 0.008 -0.930 0.352 -0.023 0.008
ar.L16 0.0043 0.007 0.652 0.514 -0.009 0.017
ar.L17 0.0190 0.006 3.162 0.002 0.007 0.031
ar.L18 -0.0058 0.013 -0.436 0.663 -0.032 0.020
ar.L19 0.0209 0.007 3.180 0.001 0.008 0.034
ar.L20 0.0030 0.014 0.206 0.837 -0.025 0.031
ar.L21 -0.0222 0.005 -4.094 0.000 -0.033 -0.012
ar.L22 0.0096 0.016 0.609 0.542 -0.021 0.040
ar.L23 0.0257 0.008 3.344 0.001 0.011 0.041
ar.L24 0.0092 0.017 0.540 0.589 -0.024 0.043
ar.L25 -0.0113 0.008 -1.408 0.159 -0.027 0.004
ar.L26 -0.0109 0.009 -1.267 0.205 -0.028 0.006
ar.L27 0.0212 0.009 2.360 0.018 0.004 0.039
ar.L28 -0.0165 0.015 -1.119 0.263 -0.045 0.012
ar.L29 0.0124 0.012 1.041 0.298 -0.011 0.036
ar.L30 -0.0069 0.009 -0.760 0.447 -0.025 0.011
ma.L1 0.0091 0.642 0.014 0.989 -1.249 1.267
sigma2 0.0447 0.000 275.985 0.000 0.044 0.045
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 338631.54
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 6.21 Skew: 0.24
Prob(H) (two-sided): 0.00 Kurtosis: 26.90
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 51.971148
14228 51.885568
14229 51.895459
14230 51.874521
14231 51.966910
14232 51.947435
14233 51.973830
14234 52.044477
14235 52.028773
14236 52.115400
Name: predicted_mean, dtype: float64
lower price upper price
14227 51.556759 52.385536
14228 51.294195 52.476940
14229 51.167740 52.623178
14230 51.030353 52.718689
14231 51.017248 52.916573
14232 50.904677 52.990193
14233 50.844906 53.102753
14234 50.833614 53.255340
14235 50.741921 53.315626
14236 50.755617 53.475182
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.19679655925987144
arima_forecast('TUPRS', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 138.3464
2023-09-20 10:00:00+03:00 137.3923
2023-09-20 11:00:00+03:00 136.2474
2023-09-20 12:00:00+03:00 135.9611
2023-09-20 13:00:00+03:00 135.9611
2023-09-20 14:00:00+03:00 136.0565
2023-09-20 15:00:00+03:00 135.5795
2023-09-20 16:00:00+03:00 134.7208
2023-09-20 17:00:00+03:00 133.9575
2023-09-20 18:00:00+03:00 134.8162
2023-09-21 09:00:00+03:00 134.0529
2023-09-21 10:00:00+03:00 133.0034
2023-09-21 11:00:00+03:00 133.2896
2023-09-21 12:00:00+03:00 133.3850
2023-09-21 13:00:00+03:00 134.5300
2023-09-21 14:00:00+03:00 137.5831
2023-09-21 15:00:00+03:00 138.1556
2023-09-21 16:00:00+03:00 139.5868
2023-09-21 17:00:00+03:00 141.0179
2023-09-21 18:00:00+03:00 141.0179
2023-09-22 09:00:00+03:00 141.2087
2023-09-22 10:00:00+03:00 145.7885
2023-09-22 11:00:00+03:00 146.3610
2023-09-22 12:00:00+03:00 147.4105
2023-09-22 13:00:00+03:00 146.6472
2023-09-22 14:00:00+03:00 145.8839
2023-09-22 15:00:00+03:00 147.0288
2023-09-22 16:00:00+03:00 147.8875
2023-09-22 17:00:00+03:00 148.0784
2023-09-22 18:00:00+03:00 147.0288
Mean of the first 10 values: price 155.96888
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 150.0820
2023-09-25 10:00:00+03:00 153.3260
2023-09-25 11:00:00+03:00 154.8526
2023-09-25 12:00:00+03:00 157.2379
2023-09-25 13:00:00+03:00 156.2837
2023-09-25 14:00:00+03:00 155.9975
2023-09-25 15:00:00+03:00 157.8103
2023-09-25 16:00:00+03:00 156.8562
2023-09-25 17:00:00+03:00 158.8598
2023-09-25 18:00:00+03:00 158.3828
price
timestamp
2018-01-02 10:00:00+03:00 0.0703
2018-01-02 11:00:00+03:00 -0.0201
2018-01-02 12:00:00+03:00 0.0101
2018-01-02 13:00:00+03:00 0.0200
2018-01-02 14:00:00+03:00 0.0302
... ...
2023-09-22 14:00:00+03:00 -0.7633
2023-09-22 15:00:00+03:00 1.1449
2023-09-22 16:00:00+03:00 0.8587
2023-09-22 17:00:00+03:00 0.1909
2023-09-22 18:00:00+03:00 -1.0496
[14226 rows x 1 columns]
ADF Statistic: -16.94374339705925
p-value: 9.574477067538152e-30
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood -4191.410
Date: Sun, 24 Dec 2023 AIC 8446.821
Time: 13:26:50 BIC 8688.831
Sample: 0 HQIC 8527.325
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.2832 0.143 1.976 0.048 0.002 0.564
ar.L2 -0.0058 0.008 -0.720 0.471 -0.022 0.010
ar.L3 0.0331 0.003 10.065 0.000 0.027 0.040
ar.L4 -0.0230 0.006 -3.831 0.000 -0.035 -0.011
ar.L5 0.0168 0.004 4.126 0.000 0.009 0.025
ar.L6 0.0612 0.004 15.615 0.000 0.053 0.069
ar.L7 -0.0020 0.010 -0.195 0.845 -0.022 0.018
ar.L8 -0.0002 0.004 -0.042 0.966 -0.009 0.008
ar.L9 0.0071 0.004 1.891 0.059 -0.000 0.015
ar.L10 0.0186 0.003 7.354 0.000 0.014 0.024
ar.L11 -0.0134 0.005 -2.868 0.004 -0.023 -0.004
ar.L12 -0.0549 0.004 -14.523 0.000 -0.062 -0.047
ar.L13 -0.0074 0.008 -0.877 0.380 -0.024 0.009
ar.L14 -0.0043 0.005 -0.908 0.364 -0.013 0.005
ar.L15 -0.0207 0.003 -6.386 0.000 -0.027 -0.014
ar.L16 -0.0222 0.005 -4.476 0.000 -0.032 -0.012
ar.L17 0.0418 0.005 8.354 0.000 0.032 0.052
ar.L18 -0.0381 0.007 -5.589 0.000 -0.051 -0.025
ar.L19 0.0173 0.005 3.174 0.002 0.007 0.028
ar.L20 -0.0070 0.003 -2.126 0.034 -0.014 -0.001
ar.L21 -0.0054 0.004 -1.482 0.138 -0.013 0.002
ar.L22 0.0446 0.004 11.482 0.000 0.037 0.052
ar.L23 0.0165 0.007 2.236 0.025 0.002 0.031
ar.L24 -0.0154 0.005 -2.830 0.005 -0.026 -0.005
ar.L25 0.0137 0.004 3.286 0.001 0.006 0.022
ar.L26 0.0187 0.004 4.617 0.000 0.011 0.027
ar.L27 -0.0213 0.005 -4.577 0.000 -0.030 -0.012
ar.L28 -0.0129 0.005 -2.550 0.011 -0.023 -0.003
ar.L29 0.0501 0.005 10.940 0.000 0.041 0.059
ar.L30 -0.0358 0.006 -5.695 0.000 -0.048 -0.023
ma.L1 -0.2322 0.143 -1.622 0.105 -0.513 0.048
sigma2 0.1055 0.000 366.833 0.000 0.105 0.106
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 2237842.44
Prob(Q): 0.93 Prob(JB): 0.00
Heteroskedasticity (H): 34.23 Skew: 2.34
Prob(H) (two-sided): 0.00 Kurtosis: 64.26
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 146.670404
14228 146.601191
14229 146.613837
14230 146.221092
14231 146.092269
14232 145.926621
14233 145.751107
14234 145.790524
14235 146.024577
14236 145.805277
Name: predicted_mean, dtype: float64
lower price upper price
14227 146.033660 147.307147
14228 145.677424 147.524958
14229 145.469883 147.757790
14230 144.881425 147.560758
14231 144.585375 147.599162
14232 144.265959 147.587283
14233 143.932903 147.569312
14234 143.822810 147.758239
14235 143.916461 148.132692
14236 143.562793 148.047760
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 10.208856797991869
arima_forecast('TTKOM', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 23.72
2023-09-20 10:00:00+03:00 23.86
2023-09-20 11:00:00+03:00 23.64
2023-09-20 12:00:00+03:00 23.40
2023-09-20 13:00:00+03:00 23.40
2023-09-20 14:00:00+03:00 23.48
2023-09-20 15:00:00+03:00 23.58
2023-09-20 16:00:00+03:00 23.24
2023-09-20 17:00:00+03:00 22.68
2023-09-20 18:00:00+03:00 22.64
2023-09-21 09:00:00+03:00 22.68
2023-09-21 10:00:00+03:00 22.56
2023-09-21 11:00:00+03:00 22.60
2023-09-21 12:00:00+03:00 22.42
2023-09-21 13:00:00+03:00 22.52
2023-09-21 14:00:00+03:00 23.12
2023-09-21 15:00:00+03:00 22.94
2023-09-21 16:00:00+03:00 22.98
2023-09-21 17:00:00+03:00 23.26
2023-09-21 18:00:00+03:00 23.34
2023-09-22 09:00:00+03:00 23.38
2023-09-22 10:00:00+03:00 23.34
2023-09-22 11:00:00+03:00 23.62
2023-09-22 12:00:00+03:00 23.48
2023-09-22 13:00:00+03:00 23.42
2023-09-22 14:00:00+03:00 23.22
2023-09-22 15:00:00+03:00 23.18
2023-09-22 16:00:00+03:00 23.22
2023-09-22 17:00:00+03:00 23.20
2023-09-22 18:00:00+03:00 23.18
Mean of the first 10 values: price 23.638
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 23.40
2023-09-25 10:00:00+03:00 23.62
2023-09-25 11:00:00+03:00 23.52
2023-09-25 12:00:00+03:00 23.70
2023-09-25 13:00:00+03:00 23.70
2023-09-25 14:00:00+03:00 23.52
2023-09-25 15:00:00+03:00 23.48
2023-09-25 16:00:00+03:00 23.56
2023-09-25 17:00:00+03:00 23.94
2023-09-25 18:00:00+03:00 23.94
price
timestamp
2018-01-02 10:00:00+03:00 0.1123
2018-01-02 11:00:00+03:00 0.0000
2018-01-02 12:00:00+03:00 -0.0161
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 0.0241
... ...
2023-09-22 14:00:00+03:00 -0.2000
2023-09-22 15:00:00+03:00 -0.0400
2023-09-22 16:00:00+03:00 0.0400
2023-09-22 17:00:00+03:00 -0.0200
2023-09-22 18:00:00+03:00 -0.0200
[14226 rows x 1 columns]
ADF Statistic: -16.887173836627433
p-value: 1.0518963206021561e-29
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 11745.489
Date: Sun, 24 Dec 2023 AIC -23426.978
Time: 02:03:00 BIC -23184.967
Sample: 0 HQIC -23346.473
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.0168 0.556 -0.030 0.976 -1.107 1.073
ar.L2 -0.0381 0.020 -1.942 0.052 -0.077 0.000
ar.L3 0.0108 0.022 0.488 0.626 -0.032 0.054
ar.L4 0.0033 0.007 0.485 0.628 -0.010 0.017
ar.L5 0.0066 0.004 1.885 0.059 -0.000 0.013
ar.L6 0.0398 0.005 7.730 0.000 0.030 0.050
ar.L7 -0.0098 0.023 -0.436 0.663 -0.054 0.034
ar.L8 -0.0126 0.006 -2.200 0.028 -0.024 -0.001
ar.L9 0.0182 0.008 2.307 0.021 0.003 0.034
ar.L10 0.0377 0.010 3.801 0.000 0.018 0.057
ar.L11 -0.0380 0.021 -1.795 0.073 -0.079 0.003
ar.L12 -0.0226 0.021 -1.087 0.277 -0.063 0.018
ar.L13 0.0267 0.014 1.973 0.048 0.000 0.053
ar.L14 0.0046 0.015 0.311 0.756 -0.025 0.034
ar.L15 0.0200 0.005 4.036 0.000 0.010 0.030
ar.L16 0.0018 0.012 0.147 0.883 -0.022 0.026
ar.L17 0.0368 0.004 9.542 0.000 0.029 0.044
ar.L18 -0.0074 0.020 -0.363 0.717 -0.047 0.033
ar.L19 0.0509 0.005 10.273 0.000 0.041 0.061
ar.L20 0.0077 0.028 0.273 0.785 -0.047 0.063
ar.L21 -0.0055 0.007 -0.840 0.401 -0.018 0.007
ar.L22 -0.0168 0.005 -3.738 0.000 -0.026 -0.008
ar.L23 0.0084 0.011 0.800 0.423 -0.012 0.029
ar.L24 -0.0072 0.006 -1.260 0.208 -0.018 0.004
ar.L25 -0.0277 0.005 -5.048 0.000 -0.038 -0.017
ar.L26 -0.0294 0.016 -1.852 0.064 -0.061 0.002
ar.L27 0.0221 0.017 1.321 0.186 -0.011 0.055
ar.L28 -0.0089 0.012 -0.725 0.469 -0.033 0.015
ar.L29 0.0083 0.006 1.329 0.184 -0.004 0.020
ar.L30 0.0061 0.006 1.109 0.267 -0.005 0.017
ma.L1 -0.0177 0.556 -0.032 0.975 -1.108 1.072
sigma2 0.0112 3.18e-05 353.129 0.000 0.011 0.011
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1538455.41
Prob(Q): 1.00 Prob(JB): 0.00
Heteroskedasticity (H): 28.54 Skew: 1.00
Prob(H) (two-sided): 0.00 Kurtosis: 53.91
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 23.168744
14228 23.169485
14229 23.204598
14230 23.219760
14231 23.254599
14232 23.259209
14233 23.261880
14234 23.278556
14235 23.278964
14236 23.284067
Name: predicted_mean, dtype: float64
lower price upper price
14227 22.961049 23.376438
14228 22.880788 23.458183
14229 22.857479 23.551717
14230 22.821439 23.618082
14231 22.810566 23.698633
14232 22.773284 23.745135
14233 22.734267 23.789493
14234 22.713234 23.843877
14235 22.679334 23.878595
14236 22.650612 23.917523
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.42976132619291785
arima_forecast('TCELL', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 54.30
2023-09-20 10:00:00+03:00 54.75
2023-09-20 11:00:00+03:00 54.25
2023-09-20 12:00:00+03:00 54.25
2023-09-20 13:00:00+03:00 54.40
2023-09-20 14:00:00+03:00 54.65
2023-09-20 15:00:00+03:00 54.35
2023-09-20 16:00:00+03:00 54.15
2023-09-20 17:00:00+03:00 53.75
2023-09-20 18:00:00+03:00 53.40
2023-09-21 09:00:00+03:00 53.10
2023-09-21 10:00:00+03:00 53.10
2023-09-21 11:00:00+03:00 52.95
2023-09-21 12:00:00+03:00 52.90
2023-09-21 13:00:00+03:00 53.15
2023-09-21 14:00:00+03:00 54.75
2023-09-21 15:00:00+03:00 54.35
2023-09-21 16:00:00+03:00 54.40
2023-09-21 17:00:00+03:00 55.40
2023-09-21 18:00:00+03:00 55.40
2023-09-22 09:00:00+03:00 55.40
2023-09-22 10:00:00+03:00 54.85
2023-09-22 11:00:00+03:00 54.75
2023-09-22 12:00:00+03:00 54.75
2023-09-22 13:00:00+03:00 54.60
2023-09-22 14:00:00+03:00 54.40
2023-09-22 15:00:00+03:00 54.50
2023-09-22 16:00:00+03:00 54.55
2023-09-22 17:00:00+03:00 54.10
2023-09-22 18:00:00+03:00 54.45
Mean of the first 10 values: price 54.815
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 54.60
2023-09-25 10:00:00+03:00 54.60
2023-09-25 11:00:00+03:00 54.45
2023-09-25 12:00:00+03:00 54.85
2023-09-25 13:00:00+03:00 54.95
2023-09-25 14:00:00+03:00 54.85
2023-09-25 15:00:00+03:00 54.65
2023-09-25 16:00:00+03:00 55.00
2023-09-25 17:00:00+03:00 55.00
2023-09-25 18:00:00+03:00 55.20
price
timestamp
2018-01-02 10:00:00+03:00 0.0392
2018-01-02 11:00:00+03:00 -0.1018
2018-01-02 12:00:00+03:00 -0.0077
2018-01-02 13:00:00+03:00 0.0548
2018-01-02 14:00:00+03:00 0.0547
... ...
2023-09-22 14:00:00+03:00 -0.2000
2023-09-22 15:00:00+03:00 0.1000
2023-09-22 16:00:00+03:00 0.0500
2023-09-22 17:00:00+03:00 -0.4500
2023-09-22 18:00:00+03:00 0.3500
[14226 rows x 1 columns]
ADF Statistic: -18.557373182184367
p-value: 2.0892203476712126e-30
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 2889.472
Date: Sun, 24 Dec 2023 AIC -5714.944
Time: 02:04:15 BIC -5472.933
Sample: 0 HQIC -5634.439
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.4563 0.093 -4.912 0.000 -0.638 -0.274
ar.L2 -0.0304 0.005 -6.482 0.000 -0.040 -0.021
ar.L3 -0.0436 0.004 -9.768 0.000 -0.052 -0.035
ar.L4 -0.0231 0.006 -4.183 0.000 -0.034 -0.012
ar.L5 -0.0108 0.005 -2.338 0.019 -0.020 -0.002
ar.L6 0.0363 0.005 7.634 0.000 0.027 0.046
ar.L7 0.0185 0.006 3.301 0.001 0.008 0.029
ar.L8 -0.0287 0.004 -6.987 0.000 -0.037 -0.021
ar.L9 0.0397 0.005 8.628 0.000 0.031 0.049
ar.L10 0.0090 0.006 1.426 0.154 -0.003 0.021
ar.L11 -0.0100 0.004 -2.668 0.008 -0.017 -0.003
ar.L12 -0.0142 0.004 -3.750 0.000 -0.022 -0.007
ar.L13 -0.0032 0.004 -0.789 0.430 -0.011 0.005
ar.L14 0.0177 0.004 4.274 0.000 0.010 0.026
ar.L15 -0.0090 0.005 -1.821 0.069 -0.019 0.001
ar.L16 -0.0073 0.005 -1.514 0.130 -0.017 0.002
ar.L17 0.0007 0.005 0.155 0.877 -0.009 0.010
ar.L18 -0.0045 0.005 -0.992 0.321 -0.013 0.004
ar.L19 -0.0002 0.004 -0.056 0.955 -0.009 0.008
ar.L20 0.0271 0.004 7.328 0.000 0.020 0.034
ar.L21 -0.0034 0.004 -0.766 0.443 -0.012 0.005
ar.L22 0.0025 0.004 0.567 0.571 -0.006 0.011
ar.L23 -0.0281 0.004 -6.933 0.000 -0.036 -0.020
ar.L24 -0.0163 0.005 -2.986 0.003 -0.027 -0.006
ar.L25 -0.0199 0.004 -4.549 0.000 -0.028 -0.011
ar.L26 -0.0229 0.005 -5.070 0.000 -0.032 -0.014
ar.L27 -0.0048 0.004 -1.145 0.252 -0.013 0.003
ar.L28 -0.0053 0.005 -1.087 0.277 -0.015 0.004
ar.L29 -0.0068 0.004 -1.514 0.130 -0.016 0.002
ar.L30 -0.0329 0.004 -8.578 0.000 -0.040 -0.025
ma.L1 0.4257 0.093 4.579 0.000 0.244 0.608
sigma2 0.0390 0.000 308.754 0.000 0.039 0.039
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 561445.59
Prob(Q): 1.00 Prob(JB): 0.00
Heteroskedasticity (H): 21.79 Skew: 0.73
Prob(H) (two-sided): 0.00 Kurtosis: 33.74
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 54.361099
14228 54.382224
14229 54.435742
14230 54.431588
14231 54.422511
14232 54.480162
14233 54.484683
14234 54.475618
14235 54.486972
14236 54.476507
Name: predicted_mean, dtype: float64
lower price upper price
14227 53.974030 54.748168
14228 53.843136 54.921312
14229 53.782542 55.088942
14230 53.688029 55.175146
14231 53.599318 55.245704
14232 53.585283 55.375042
14233 53.517478 55.451887
14234 53.441151 55.510085
14235 53.393220 55.580723
14236 53.320252 55.632761
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.42234389490438423
arima_forecast('HALKB', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 15.39
2023-09-20 10:00:00+03:00 15.59
2023-09-20 11:00:00+03:00 15.67
2023-09-20 12:00:00+03:00 15.68
2023-09-20 13:00:00+03:00 15.67
2023-09-20 14:00:00+03:00 15.52
2023-09-20 15:00:00+03:00 15.57
2023-09-20 16:00:00+03:00 15.55
2023-09-20 17:00:00+03:00 15.36
2023-09-20 18:00:00+03:00 15.38
2023-09-21 09:00:00+03:00 15.38
2023-09-21 10:00:00+03:00 15.61
2023-09-21 11:00:00+03:00 15.50
2023-09-21 12:00:00+03:00 15.51
2023-09-21 13:00:00+03:00 15.66
2023-09-21 14:00:00+03:00 15.34
2023-09-21 15:00:00+03:00 15.34
2023-09-21 16:00:00+03:00 15.41
2023-09-21 17:00:00+03:00 15.51
2023-09-21 18:00:00+03:00 15.50
2023-09-22 09:00:00+03:00 15.59
2023-09-22 10:00:00+03:00 15.45
2023-09-22 11:00:00+03:00 15.47
2023-09-22 12:00:00+03:00 15.44
2023-09-22 13:00:00+03:00 15.50
2023-09-22 14:00:00+03:00 15.36
2023-09-22 15:00:00+03:00 15.32
2023-09-22 16:00:00+03:00 15.32
2023-09-22 17:00:00+03:00 15.20
2023-09-22 18:00:00+03:00 15.23
Mean of the first 10 values: price 15.467
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 15.32
2023-09-25 10:00:00+03:00 15.44
2023-09-25 11:00:00+03:00 15.45
2023-09-25 12:00:00+03:00 15.47
2023-09-25 13:00:00+03:00 15.67
2023-09-25 14:00:00+03:00 15.47
2023-09-25 15:00:00+03:00 15.41
2023-09-25 16:00:00+03:00 15.50
2023-09-25 17:00:00+03:00 15.49
2023-09-25 18:00:00+03:00 15.45
price
timestamp
2018-01-02 10:00:00+03:00 0.3219
2018-01-02 11:00:00+03:00 -0.0195
2018-01-02 12:00:00+03:00 -0.0976
2018-01-02 13:00:00+03:00 0.0195
2018-01-02 14:00:00+03:00 0.0586
... ...
2023-09-22 14:00:00+03:00 -0.1400
2023-09-22 15:00:00+03:00 -0.0400
2023-09-22 16:00:00+03:00 0.0000
2023-09-22 17:00:00+03:00 -0.1200
2023-09-22 18:00:00+03:00 0.0300
[14226 rows x 1 columns]
ADF Statistic: -16.312651299834577
p-value: 3.1847512340477354e-29
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 15534.967
Date: Sun, 24 Dec 2023 AIC -31005.933
Time: 02:05:16 BIC -30763.923
Sample: 0 HQIC -30925.428
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0231 0.129 0.178 0.859 -0.231 0.277
ar.L2 0.0066 0.008 0.836 0.403 -0.009 0.022
ar.L3 0.0365 0.004 8.246 0.000 0.028 0.045
ar.L4 -0.0015 0.007 -0.226 0.821 -0.015 0.012
ar.L5 -0.0133 0.005 -2.521 0.012 -0.024 -0.003
ar.L6 0.0155 0.005 3.136 0.002 0.006 0.025
ar.L7 -0.0115 0.005 -2.158 0.031 -0.022 -0.001
ar.L8 -0.0013 0.005 -0.257 0.797 -0.011 0.008
ar.L9 0.0173 0.004 4.001 0.000 0.009 0.026
ar.L10 0.0327 0.005 7.169 0.000 0.024 0.042
ar.L11 0.0068 0.006 1.177 0.239 -0.005 0.018
ar.L12 0.0038 0.004 0.868 0.385 -0.005 0.012
ar.L13 -0.0107 0.005 -2.253 0.024 -0.020 -0.001
ar.L14 0.0257 0.006 4.585 0.000 0.015 0.037
ar.L15 -0.0003 0.006 -0.050 0.960 -0.013 0.012
ar.L16 0.0048 0.006 0.876 0.381 -0.006 0.016
ar.L17 0.0011 0.005 0.246 0.806 -0.008 0.010
ar.L18 -0.0307 0.005 -6.342 0.000 -0.040 -0.021
ar.L19 0.0137 0.005 2.675 0.007 0.004 0.024
ar.L20 0.0389 0.004 9.322 0.000 0.031 0.047
ar.L21 0.0441 0.007 6.136 0.000 0.030 0.058
ar.L22 -0.0138 0.007 -1.965 0.049 -0.028 -3.27e-05
ar.L23 -0.0182 0.005 -3.407 0.001 -0.029 -0.008
ar.L24 0.0131 0.007 1.987 0.047 0.000 0.026
ar.L25 -0.0197 0.006 -3.265 0.001 -0.032 -0.008
ar.L26 0.0297 0.006 5.188 0.000 0.018 0.041
ar.L27 0.0147 0.006 2.447 0.014 0.003 0.026
ar.L28 -0.0009 0.006 -0.149 0.881 -0.012 0.011
ar.L29 -0.0109 0.005 -2.066 0.039 -0.021 -0.001
ar.L30 0.0324 0.004 8.514 0.000 0.025 0.040
ma.L1 0.0228 0.130 0.176 0.861 -0.232 0.277
sigma2 0.0066 1.96e-05 336.839 0.000 0.007 0.007
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 2000231.22
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 4.25 Skew: 0.52
Prob(H) (two-sided): 0.00 Kurtosis: 61.08
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 15.248607
14228 15.248312
14229 15.253162
14230 15.263828
14231 15.259825
14232 15.242137
14233 15.219299
14234 15.229929
14235 15.234112
14236 15.231369
Name: predicted_mean, dtype: float64
lower price upper price
14227 15.089483 15.407731
14228 15.018058 15.478567
14229 14.968349 15.537975
14230 14.930324 15.597331
14231 14.883807 15.635843
14232 14.828798 15.655475
14233 14.770765 15.667833
14234 14.749379 15.710478
14235 14.723701 15.744524
14236 14.691818 15.770920
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.2378744053801174
arima_forecast('ISCTR', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 22.20
2023-09-20 10:00:00+03:00 22.26
2023-09-20 11:00:00+03:00 22.80
2023-09-20 12:00:00+03:00 22.72
2023-09-20 13:00:00+03:00 22.74
2023-09-20 14:00:00+03:00 22.74
2023-09-20 15:00:00+03:00 22.86
2023-09-20 16:00:00+03:00 22.80
2023-09-20 17:00:00+03:00 22.50
2023-09-20 18:00:00+03:00 22.50
2023-09-21 09:00:00+03:00 22.54
2023-09-21 10:00:00+03:00 22.74
2023-09-21 11:00:00+03:00 22.70
2023-09-21 12:00:00+03:00 22.76
2023-09-21 13:00:00+03:00 22.96
2023-09-21 14:00:00+03:00 22.66
2023-09-21 15:00:00+03:00 22.72
2023-09-21 16:00:00+03:00 22.98
2023-09-21 17:00:00+03:00 23.00
2023-09-21 18:00:00+03:00 23.14
2023-09-22 09:00:00+03:00 23.18
2023-09-22 10:00:00+03:00 23.16
2023-09-22 11:00:00+03:00 23.58
2023-09-22 12:00:00+03:00 23.48
2023-09-22 13:00:00+03:00 24.40
2023-09-22 14:00:00+03:00 24.24
2023-09-22 15:00:00+03:00 24.08
2023-09-22 16:00:00+03:00 24.78
2023-09-22 17:00:00+03:00 24.96
2023-09-22 18:00:00+03:00 24.90
Mean of the first 10 values: price 25.218
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 25.04
2023-09-25 10:00:00+03:00 25.04
2023-09-25 11:00:00+03:00 25.28
2023-09-25 12:00:00+03:00 25.20
2023-09-25 13:00:00+03:00 25.24
2023-09-25 14:00:00+03:00 25.40
2023-09-25 15:00:00+03:00 25.32
2023-09-25 16:00:00+03:00 25.28
2023-09-25 17:00:00+03:00 25.22
2023-09-25 18:00:00+03:00 25.16
price
timestamp
2018-01-02 10:00:00+03:00 0.0113
2018-01-02 11:00:00+03:00 0.0000
2018-01-02 12:00:00+03:00 -0.0075
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 0.0300
... ...
2023-09-22 14:00:00+03:00 -0.1600
2023-09-22 15:00:00+03:00 -0.1600
2023-09-22 16:00:00+03:00 0.7000
2023-09-22 17:00:00+03:00 0.1800
2023-09-22 18:00:00+03:00 -0.0600
[14226 rows x 1 columns]
ADF Statistic: -15.356857191577735
p-value: 3.6568880200845955e-28
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 17865.450
Date: Sun, 24 Dec 2023 AIC -35666.900
Time: 02:05:54 BIC -35424.889
Sample: 0 HQIC -35586.395
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0170 13.553 0.001 0.999 -26.546 26.580
ar.L2 -0.0337 0.491 -0.069 0.945 -0.997 0.929
ar.L3 0.0336 0.467 0.072 0.943 -0.882 0.949
ar.L4 -0.0081 0.464 -0.017 0.986 -0.917 0.901
ar.L5 0.0352 0.118 0.297 0.766 -0.197 0.267
ar.L6 0.0220 0.479 0.046 0.963 -0.917 0.961
ar.L7 0.0078 0.289 0.027 0.978 -0.558 0.574
ar.L8 0.0234 0.101 0.232 0.817 -0.174 0.221
ar.L9 -0.0027 0.315 -0.009 0.993 -0.621 0.615
ar.L10 0.0441 0.043 1.039 0.299 -0.039 0.127
ar.L11 0.0301 0.599 0.050 0.960 -1.144 1.204
ar.L12 -0.0145 0.396 -0.037 0.971 -0.791 0.762
ar.L13 0.0181 0.204 0.089 0.929 -0.381 0.417
ar.L14 0.0045 0.250 0.018 0.986 -0.485 0.494
ar.L15 0.0056 0.056 0.100 0.920 -0.105 0.116
ar.L16 -0.0070 0.075 -0.093 0.926 -0.155 0.140
ar.L17 0.0462 0.097 0.478 0.632 -0.143 0.236
ar.L18 0.0203 0.629 0.032 0.974 -1.212 1.253
ar.L19 0.0025 0.264 0.010 0.992 -0.515 0.520
ar.L20 0.0235 0.029 0.810 0.418 -0.033 0.080
ar.L21 -0.0104 0.317 -0.033 0.974 -0.633 0.612
ar.L22 0.0055 0.148 0.037 0.970 -0.284 0.295
ar.L23 0.0006 0.078 0.008 0.994 -0.152 0.154
ar.L24 0.0098 0.008 1.234 0.217 -0.006 0.025
ar.L25 -0.0050 0.132 -0.038 0.970 -0.264 0.254
ar.L26 0.0252 0.071 0.355 0.723 -0.114 0.165
ar.L27 0.0190 0.343 0.055 0.956 -0.654 0.692
ar.L28 -0.0224 0.251 -0.089 0.929 -0.515 0.470
ar.L29 -0.0217 0.308 -0.071 0.944 -0.625 0.582
ar.L30 -0.0002 0.289 -0.001 1.000 -0.566 0.565
ma.L1 0.0193 13.553 0.001 0.999 -26.544 26.582
sigma2 0.0047 1.26e-05 376.543 0.000 0.005 0.005
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 2170716.18
Prob(Q): 0.98 Prob(JB): 0.00
Heteroskedasticity (H): 51.71 Skew: 1.07
Prob(H) (two-sided): 0.00 Kurtosis: 63.48
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 24.935824
14228 24.939414
14229 24.986645
14230 25.013415
14231 25.053582
14232 25.094191
14233 25.092154
14234 25.160995
14235 25.197084
14236 25.207361
Name: predicted_mean, dtype: float64
lower price upper price
14227 24.800746 25.070902
14228 24.744889 25.133939
14229 24.749578 25.223712
14230 24.738199 25.288631
14231 24.745213 25.361950
14232 24.754067 25.434314
14233 24.721654 25.462655
14234 24.762108 25.559882
14235 24.770541 25.623627
14236 24.754885 25.659837
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.18410772858785132
arima_forecast('VAKBN', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 13.60
2023-09-20 10:00:00+03:00 13.81
2023-09-20 11:00:00+03:00 13.91
2023-09-20 12:00:00+03:00 13.86
2023-09-20 13:00:00+03:00 13.89
2023-09-20 14:00:00+03:00 13.83
2023-09-20 15:00:00+03:00 13.85
2023-09-20 16:00:00+03:00 13.77
2023-09-20 17:00:00+03:00 13.52
2023-09-20 18:00:00+03:00 13.46
2023-09-21 09:00:00+03:00 13.46
2023-09-21 10:00:00+03:00 13.62
2023-09-21 11:00:00+03:00 13.54
2023-09-21 12:00:00+03:00 13.58
2023-09-21 13:00:00+03:00 13.69
2023-09-21 14:00:00+03:00 13.52
2023-09-21 15:00:00+03:00 13.49
2023-09-21 16:00:00+03:00 13.59
2023-09-21 17:00:00+03:00 13.63
2023-09-21 18:00:00+03:00 13.64
2023-09-22 09:00:00+03:00 13.65
2023-09-22 10:00:00+03:00 13.69
2023-09-22 11:00:00+03:00 13.75
2023-09-22 12:00:00+03:00 13.70
2023-09-22 13:00:00+03:00 13.79
2023-09-22 14:00:00+03:00 13.58
2023-09-22 15:00:00+03:00 13.53
2023-09-22 16:00:00+03:00 13.56
2023-09-22 17:00:00+03:00 13.49
2023-09-22 18:00:00+03:00 13.50
Mean of the first 10 values: price 13.799
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 13.59
2023-09-25 10:00:00+03:00 13.64
2023-09-25 11:00:00+03:00 13.72
2023-09-25 12:00:00+03:00 13.85
2023-09-25 13:00:00+03:00 13.93
2023-09-25 14:00:00+03:00 13.85
2023-09-25 15:00:00+03:00 13.80
2023-09-25 16:00:00+03:00 13.86
2023-09-25 17:00:00+03:00 13.85
2023-09-25 18:00:00+03:00 13.90
price
timestamp
2018-01-02 10:00:00+03:00 0.1085
2018-01-02 11:00:00+03:00 0.0788
2018-01-02 12:00:00+03:00 0.0000
2018-01-02 13:00:00+03:00 0.0099
2018-01-02 14:00:00+03:00 0.0394
... ...
2023-09-22 14:00:00+03:00 -0.2100
2023-09-22 15:00:00+03:00 -0.0500
2023-09-22 16:00:00+03:00 0.0300
2023-09-22 17:00:00+03:00 -0.0700
2023-09-22 18:00:00+03:00 0.0100
[14226 rows x 1 columns]
ADF Statistic: -15.690217287205973
p-value: 1.4368998336235054e-28
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 18049.059
Date: Sun, 24 Dec 2023 AIC -36034.118
Time: 02:06:49 BIC -35792.108
Sample: 0 HQIC -35953.614
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -7.69e-05 0.187 -0.000 1.000 -0.367 0.367
ar.L2 0.0119 0.004 2.755 0.006 0.003 0.020
ar.L3 0.0481 0.005 10.343 0.000 0.039 0.057
ar.L4 -0.0224 0.010 -2.282 0.022 -0.042 -0.003
ar.L5 -0.0030 0.006 -0.488 0.626 -0.015 0.009
ar.L6 0.0116 0.004 2.613 0.009 0.003 0.020
ar.L7 -0.0043 0.005 -0.875 0.382 -0.014 0.005
ar.L8 0.0162 0.004 4.056 0.000 0.008 0.024
ar.L9 0.0092 0.005 1.847 0.065 -0.001 0.019
ar.L10 0.0570 0.003 17.729 0.000 0.051 0.063
ar.L11 0.0097 0.012 0.793 0.428 -0.014 0.034
ar.L12 -0.0157 0.006 -2.790 0.005 -0.027 -0.005
ar.L13 -0.0108 0.006 -1.828 0.068 -0.022 0.001
ar.L14 0.0286 0.006 4.950 0.000 0.017 0.040
ar.L15 -0.0020 0.007 -0.280 0.779 -0.016 0.012
ar.L16 0.0031 0.005 0.684 0.494 -0.006 0.012
ar.L17 0.0038 0.005 0.767 0.443 -0.006 0.014
ar.L18 -0.0167 0.004 -3.909 0.000 -0.025 -0.008
ar.L19 0.0409 0.005 7.693 0.000 0.030 0.051
ar.L20 0.0725 0.009 8.434 0.000 0.056 0.089
ar.L21 0.0067 0.016 0.424 0.672 -0.024 0.038
ar.L22 -0.0117 0.004 -2.859 0.004 -0.020 -0.004
ar.L23 -0.0193 0.005 -4.009 0.000 -0.029 -0.010
ar.L24 0.0091 0.007 1.290 0.197 -0.005 0.023
ar.L25 -0.0098 0.005 -1.819 0.069 -0.020 0.001
ar.L26 0.0227 0.005 4.136 0.000 0.012 0.034
ar.L27 0.0110 0.007 1.683 0.092 -0.002 0.024
ar.L28 0.0113 0.004 2.626 0.009 0.003 0.020
ar.L29 0.0364 0.005 7.095 0.000 0.026 0.046
ar.L30 0.0254 0.007 3.434 0.001 0.011 0.040
ma.L1 -0.0007 0.187 -0.004 0.997 -0.368 0.367
sigma2 0.0046 1.27e-05 364.648 0.000 0.005 0.005
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 2785477.15
Prob(Q): 0.97 Prob(JB): 0.00
Heteroskedasticity (H): 8.23 Skew: 0.11
Prob(H) (two-sided): 0.00 Kurtosis: 71.55
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 13.527284
14228 13.544700
14229 13.547655
14230 13.551834
14231 13.544900
14232 13.517635
14233 13.502864
14234 13.505235
14235 13.506193
14236 13.498380
Name: predicted_mean, dtype: float64
lower price upper price
14227 13.393939 13.660630
14228 13.356193 13.733206
14229 13.315891 13.779419
14230 13.280424 13.823243
14231 13.240301 13.849500
14232 13.183223 13.852048
14233 13.140404 13.865323
14234 13.117059 13.893410
14235 13.093144 13.919242
14236 13.061421 13.935338
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.29732815327071493
arima_forecast('VESTL', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 61.45
2023-09-20 10:00:00+03:00 62.15
2023-09-20 11:00:00+03:00 61.65
2023-09-20 12:00:00+03:00 61.85
2023-09-20 13:00:00+03:00 61.35
2023-09-20 14:00:00+03:00 61.05
2023-09-20 15:00:00+03:00 60.85
2023-09-20 16:00:00+03:00 60.50
2023-09-20 17:00:00+03:00 59.90
2023-09-20 18:00:00+03:00 59.50
2023-09-21 09:00:00+03:00 59.05
2023-09-21 10:00:00+03:00 58.75
2023-09-21 11:00:00+03:00 59.60
2023-09-21 12:00:00+03:00 59.15
2023-09-21 13:00:00+03:00 59.35
2023-09-21 14:00:00+03:00 60.35
2023-09-21 15:00:00+03:00 60.05
2023-09-21 16:00:00+03:00 60.95
2023-09-21 17:00:00+03:00 61.70
2023-09-21 18:00:00+03:00 61.90
2023-09-22 09:00:00+03:00 62.05
2023-09-22 10:00:00+03:00 62.55
2023-09-22 11:00:00+03:00 63.40
2023-09-22 12:00:00+03:00 63.10
2023-09-22 13:00:00+03:00 63.10
2023-09-22 14:00:00+03:00 63.10
2023-09-22 15:00:00+03:00 62.55
2023-09-22 16:00:00+03:00 64.15
2023-09-22 17:00:00+03:00 64.20
2023-09-22 18:00:00+03:00 64.10
Mean of the first 10 values: price 64.705
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 64.40
2023-09-25 10:00:00+03:00 65.10
2023-09-25 11:00:00+03:00 64.80
2023-09-25 12:00:00+03:00 64.75
2023-09-25 13:00:00+03:00 65.00
2023-09-25 14:00:00+03:00 64.45
2023-09-25 15:00:00+03:00 64.30
2023-09-25 16:00:00+03:00 64.70
2023-09-25 17:00:00+03:00 64.80
2023-09-25 18:00:00+03:00 64.75
price
timestamp
2018-01-02 10:00:00+03:00 0.1733
2018-01-02 11:00:00+03:00 0.3150
2018-01-02 12:00:00+03:00 0.0393
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 0.3229
... ...
2023-09-22 14:00:00+03:00 0.0000
2023-09-22 15:00:00+03:00 -0.5500
2023-09-22 16:00:00+03:00 1.6000
2023-09-22 17:00:00+03:00 0.0500
2023-09-22 18:00:00+03:00 -0.1000
[14226 rows x 1 columns]
ADF Statistic: -17.04161623822715
p-value: 8.188635177351237e-30
Critical Values: {'1%': -3.430811052000141, '5%': -2.8617437641803356, '10%': -2.566878458903999}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood -3104.699
Date: Sun, 24 Dec 2023 AIC 6273.399
Time: 02:08:31 BIC 6515.409
Sample: 0 HQIC 6353.903
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.6190 0.066 9.352 0.000 0.489 0.749
ar.L2 0.0048 0.004 1.239 0.215 -0.003 0.012
ar.L3 0.0117 0.004 2.857 0.004 0.004 0.020
ar.L4 -0.0082 0.004 -1.856 0.064 -0.017 0.000
ar.L5 0.0018 0.004 0.463 0.643 -0.006 0.010
ar.L6 0.0394 0.004 10.300 0.000 0.032 0.047
ar.L7 -0.0205 0.005 -4.160 0.000 -0.030 -0.011
ar.L8 0.0147 0.004 3.645 0.000 0.007 0.023
ar.L9 0.0320 0.004 7.342 0.000 0.023 0.041
ar.L10 -0.0425 0.005 -9.389 0.000 -0.051 -0.034
ar.L11 -0.0066 0.004 -1.797 0.072 -0.014 0.001
ar.L12 -0.0142 0.005 -2.918 0.004 -0.024 -0.005
ar.L13 0.0051 0.004 1.160 0.246 -0.004 0.014
ar.L14 -0.0630 0.004 -14.078 0.000 -0.072 -0.054
ar.L15 0.0340 0.006 5.486 0.000 0.022 0.046
ar.L16 0.0176 0.004 4.301 0.000 0.010 0.026
ar.L17 0.0131 0.005 2.880 0.004 0.004 0.022
ar.L18 -0.0104 0.005 -2.060 0.039 -0.020 -0.001
ar.L19 -0.0002 0.004 -0.036 0.971 -0.008 0.008
ar.L20 0.0518 0.004 13.638 0.000 0.044 0.059
ar.L21 -0.0500 0.005 -9.547 0.000 -0.060 -0.040
ar.L22 -0.0076 0.005 -1.496 0.135 -0.017 0.002
ar.L23 0.0194 0.005 3.952 0.000 0.010 0.029
ar.L24 0.0121 0.004 2.757 0.006 0.003 0.021
ar.L25 -0.0233 0.005 -5.124 0.000 -0.032 -0.014
ar.L26 0.0109 0.005 2.371 0.018 0.002 0.020
ar.L27 -0.0089 0.004 -2.057 0.040 -0.017 -0.000
ar.L28 0.0110 0.005 2.140 0.032 0.001 0.021
ar.L29 0.0135 0.005 2.654 0.008 0.004 0.023
ar.L30 0.0150 0.004 3.469 0.001 0.007 0.023
ma.L1 -0.6222 0.066 -9.366 0.000 -0.752 -0.492
sigma2 0.0906 0.000 346.291 0.000 0.090 0.091
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 801231.75
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 36.06 Skew: 0.46
Prob(H) (two-sided): 0.00 Kurtosis: 39.75
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 64.140910
14228 64.117436
14229 64.067607
14230 64.055273
14231 64.032753
14232 64.064191
14233 64.047754
14234 64.043630
14235 64.063292
14236 63.995251
Name: predicted_mean, dtype: float64
lower price upper price
14227 63.550996 64.730825
14228 63.284536 64.950335
14229 63.047130 65.088084
14230 62.872723 65.237822
14231 62.707798 65.357708
14232 62.610236 65.518146
14233 62.466040 65.629469
14234 62.342756 65.744504
14235 62.247398 65.879186
14236 62.062461 65.928041
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.6886839822057449
arima_forecast('YKBNK', 30, 1, 1)
price
timestamp
2023-09-20 09:00:00+03:00 16.15
2023-09-20 10:00:00+03:00 16.42
2023-09-20 11:00:00+03:00 16.55
2023-09-20 12:00:00+03:00 16.59
2023-09-20 13:00:00+03:00 16.64
2023-09-20 14:00:00+03:00 16.64
2023-09-20 15:00:00+03:00 16.89
2023-09-20 16:00:00+03:00 16.85
2023-09-20 17:00:00+03:00 16.52
2023-09-20 18:00:00+03:00 16.51
2023-09-21 09:00:00+03:00 16.51
2023-09-21 10:00:00+03:00 16.78
2023-09-21 11:00:00+03:00 16.69
2023-09-21 12:00:00+03:00 16.74
2023-09-21 13:00:00+03:00 16.83
2023-09-21 14:00:00+03:00 16.77
2023-09-21 15:00:00+03:00 16.85
2023-09-21 16:00:00+03:00 16.92
2023-09-21 17:00:00+03:00 16.96
2023-09-21 18:00:00+03:00 17.00
2023-09-22 09:00:00+03:00 17.00
2023-09-22 10:00:00+03:00 16.90
2023-09-22 11:00:00+03:00 17.01
2023-09-22 12:00:00+03:00 16.98
2023-09-22 13:00:00+03:00 17.06
2023-09-22 14:00:00+03:00 16.91
2023-09-22 15:00:00+03:00 16.93
2023-09-22 16:00:00+03:00 16.99
2023-09-22 17:00:00+03:00 16.81
2023-09-22 18:00:00+03:00 16.80
Mean of the first 10 values: price 17.015
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 16.89
2023-09-25 10:00:00+03:00 16.90
2023-09-25 11:00:00+03:00 16.91
2023-09-25 12:00:00+03:00 17.02
2023-09-25 13:00:00+03:00 17.09
2023-09-25 14:00:00+03:00 17.02
2023-09-25 15:00:00+03:00 17.02
2023-09-25 16:00:00+03:00 17.12
2023-09-25 17:00:00+03:00 17.08
2023-09-25 18:00:00+03:00 17.10
price
timestamp
2018-01-02 10:00:00+03:00 0.0169
2018-01-02 11:00:00+03:00 0.0169
2018-01-02 12:00:00+03:00 0.0000
2018-01-02 13:00:00+03:00 0.0000
2018-01-02 14:00:00+03:00 0.0167
... ...
2023-09-22 14:00:00+03:00 -0.1500
2023-09-22 15:00:00+03:00 0.0200
2023-09-22 16:00:00+03:00 0.0600
2023-09-22 17:00:00+03:00 -0.1800
2023-09-22 18:00:00+03:00 -0.0100
[14226 rows x 1 columns]
ADF Statistic: -17.782246906761788
p-value: 3.273386116327592e-30
Critical Values: {'1%': -3.43081108450881, '5%': -2.861743778546585, '10%': -2.5668784665510107}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 14227
Model: ARIMA(30, 1, 1) Log Likelihood 20865.211
Date: Sun, 24 Dec 2023 AIC -41666.422
Time: 18:28:01 BIC -41424.412
Sample: 0 HQIC -41585.917
- 14227
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0073 1.138 0.006 0.995 -2.224 2.239
ar.L2 -0.0077 0.017 -0.443 0.658 -0.042 0.026
ar.L3 0.0301 0.010 3.072 0.002 0.011 0.049
ar.L4 0.0312 0.034 0.913 0.361 -0.036 0.098
ar.L5 0.0084 0.035 0.238 0.812 -0.061 0.078
ar.L6 0.0181 0.010 1.766 0.077 -0.002 0.038
ar.L7 -0.0183 0.021 -0.882 0.378 -0.059 0.022
ar.L8 0.0029 0.021 0.137 0.891 -0.039 0.045
ar.L9 0.0229 0.005 4.802 0.000 0.014 0.032
ar.L10 -0.0079 0.026 -0.302 0.763 -0.059 0.043
ar.L11 -0.0056 0.010 -0.557 0.578 -0.025 0.014
ar.L12 -0.0177 0.008 -2.200 0.028 -0.034 -0.002
ar.L13 -0.0084 0.020 -0.410 0.682 -0.048 0.032
ar.L14 -0.0125 0.010 -1.233 0.218 -0.032 0.007
ar.L15 0.0007 0.015 0.050 0.960 -0.028 0.029
ar.L16 -0.0074 0.004 -1.703 0.088 -0.016 0.001
ar.L17 0.0200 0.009 2.190 0.029 0.002 0.038
ar.L18 0.0112 0.023 0.487 0.626 -0.034 0.056
ar.L19 0.0077 0.013 0.590 0.555 -0.018 0.033
ar.L20 0.0073 0.009 0.781 0.435 -0.011 0.026
ar.L21 -0.0259 0.009 -2.864 0.004 -0.044 -0.008
ar.L22 -0.0028 0.030 -0.093 0.926 -0.061 0.056
ar.L23 0.0039 0.005 0.795 0.426 -0.006 0.014
ar.L24 -0.0045 0.006 -0.706 0.480 -0.017 0.008
ar.L25 -0.0351 0.006 -5.455 0.000 -0.048 -0.022
ar.L26 0.0131 0.040 0.324 0.746 -0.066 0.092
ar.L27 0.0385 0.016 2.416 0.016 0.007 0.070
ar.L28 -0.0155 0.044 -0.353 0.724 -0.101 0.070
ar.L29 0.0207 0.018 1.130 0.258 -0.015 0.057
ar.L30 -0.0035 0.024 -0.144 0.885 -0.050 0.043
ma.L1 0.0076 1.138 0.007 0.995 -2.224 2.239
sigma2 0.0031 8.44e-06 369.197 0.000 0.003 0.003
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1623145.19
Prob(Q): 0.98 Prob(JB): 0.00
Heteroskedasticity (H): 37.55 Skew: -0.33
Prob(H) (two-sided): 0.00 Kurtosis: 55.32
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227 16.806804
14228 16.798024
14229 16.791736
14230 16.817846
14231 16.801339
14232 16.796330
14233 16.793985
14234 16.792064
14235 16.795939
14236 16.788944
Name: predicted_mean, dtype: float64
lower price upper price
14227 16.697404 16.916204
14228 16.642155 16.953893
14229 16.600846 16.982625
14230 16.595777 17.039915
14231 16.550353 17.052326
14232 16.519028 17.073633
14233 16.491851 17.096119
14234 16.467634 17.116494
14235 16.450509 17.141369
14236 16.422826 17.155061
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.23272251923593065
# Define forecast_values_dict outside the function
forecast_values_dict = {}
forecastplusyahoo('THYAO', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 21.04 THYAO
2021-12-27 10:00:00+03:00 21.54 THYAO
2021-12-27 11:00:00+03:00 21.20 THYAO
2021-12-27 12:00:00+03:00 21.18 THYAO
2021-12-27 13:00:00+03:00 21.14 THYAO
... ... ...
2023-09-22 14:00:00+03:00 228.80 THYAO
2023-09-22 15:00:00+03:00 227.90 THYAO
2023-09-22 16:00:00+03:00 228.20 THYAO
2023-09-22 17:00:00+03:00 226.80 THYAO
2023-09-22 18:00:00+03:00 226.10 THYAO
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for THYAO.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 21.040000
2021-12-27 06:30:00+00:00 21.299999
2021-12-27 07:00:00+00:00 21.540000
2021-12-27 07:30:00+00:00 21.160000
2021-12-27 08:00:00+00:00 21.200000
2021-12-27 08:30:00+00:00 21.299999
2021-12-27 09:00:00+00:00 21.180000
2021-12-27 09:30:00+00:00 21.080000
2021-12-27 10:00:00+00:00 21.140000
2021-12-27 10:30:00+00:00 21.120001
2021-12-27 11:00:00+00:00 20.880000
2021-12-27 11:30:00+00:00 20.860001
2021-12-27 12:00:00+00:00 20.860000
2021-12-27 12:30:00+00:00 20.840000
2021-12-27 13:00:00+00:00 21.040000
2021-12-27 13:30:00+00:00 21.299999
2021-12-27 14:00:00+00:00 20.780000
2021-12-27 15:00:00+00:00 20.780000
2021-12-28 06:00:00+00:00 20.860000
2021-12-28 06:30:00+00:00 20.780001
2021-12-28 07:00:00+00:00 21.060000
2021-12-28 07:30:00+00:00 21.059999
2021-12-28 08:00:00+00:00 20.940000
2021-12-28 08:30:00+00:00 20.940001
2021-12-28 09:00:00+00:00 20.980000
2021-12-28 09:30:00+00:00 21.040001
2021-12-28 10:00:00+00:00 20.920000
2021-12-28 10:30:00+00:00 20.780001
2021-12-28 11:00:00+00:00 20.780000
2021-12-28 11:30:00+00:00 20.799999
2021-12-28 12:00:00+00:00 20.780000
2021-12-28 12:30:00+00:00 20.719999
2021-12-28 13:00:00+00:00 20.480000
2021-12-28 13:30:00+00:00 20.180000
2021-12-28 14:00:00+00:00 20.600000
2021-12-28 15:00:00+00:00 20.480000
2021-12-29 06:00:00+00:00 20.080000
2021-12-29 06:30:00+00:00 19.889999
2021-12-29 07:00:00+00:00 20.040000
2021-12-29 07:30:00+00:00 20.360001
2021-12-29 08:00:00+00:00 20.700000
2021-12-29 08:30:00+00:00 20.680000
2021-12-29 09:00:00+00:00 20.880000
2021-12-29 09:30:00+00:00 20.760000
2021-12-29 10:00:00+00:00 20.860000
2021-12-29 10:30:00+00:00 20.760000
2021-12-29 11:00:00+00:00 20.940000
2021-12-29 11:30:00+00:00 21.120001
2021-12-29 12:00:00+00:00 21.100000
2021-12-29 12:30:00+00:00 21.240000
price
2023-09-21 10:00:00+00:00 221.400000
2023-09-21 10:30:00+00:00 225.500000
2023-09-21 11:00:00+00:00 227.900000
2023-09-21 11:30:00+00:00 227.600006
2023-09-21 12:00:00+00:00 226.600000
2023-09-21 12:30:00+00:00 229.100006
2023-09-21 13:00:00+00:00 228.600000
2023-09-21 13:30:00+00:00 231.000000
2023-09-21 14:00:00+00:00 232.900000
2023-09-21 14:30:00+00:00 232.899994
2023-09-21 15:00:00+00:00 232.600000
2023-09-22 06:00:00+00:00 233.000000
2023-09-22 06:30:00+00:00 232.800003
2023-09-22 07:00:00+00:00 231.100000
2023-09-22 07:30:00+00:00 231.800003
2023-09-22 08:00:00+00:00 232.200000
2023-09-22 08:30:00+00:00 231.800003
2023-09-22 09:00:00+00:00 231.700000
2023-09-22 09:30:00+00:00 231.199997
2023-09-22 10:00:00+00:00 230.700000
2023-09-22 10:30:00+00:00 229.399994
2023-09-22 11:00:00+00:00 228.800000
2023-09-22 11:30:00+00:00 227.500000
2023-09-22 12:00:00+00:00 227.900000
2023-09-22 12:30:00+00:00 228.199997
2023-09-22 13:00:00+00:00 228.200000
2023-09-22 13:30:00+00:00 227.000000
2023-09-22 14:00:00+00:00 226.800000
2023-09-22 14:30:00+00:00 226.800003
2023-09-22 15:00:00+00:00 226.100000
Mean of the first 10 values: price 229.86
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 227.0
2023-09-25 10:00:00+03:00 226.9
2023-09-25 11:00:00+03:00 228.5
2023-09-25 12:00:00+03:00 229.7
2023-09-25 13:00:00+03:00 231.0
2023-09-25 14:00:00+03:00 229.9
2023-09-25 15:00:00+03:00 229.0
2023-09-25 16:00:00+03:00 231.3
2023-09-25 17:00:00+03:00 232.8
2023-09-25 18:00:00+03:00 232.5
price
2018-01-02 10:00:00+03:00 0.220000
2018-01-02 11:00:00+03:00 0.040000
2018-01-02 12:00:00+03:00 0.000000
2018-01-02 13:00:00+03:00 0.010000
2018-01-02 14:00:00+03:00 -0.010000
... ...
2023-09-22 13:00:00+00:00 0.000003
2023-09-22 13:30:00+00:00 -1.200000
2023-09-22 14:00:00+00:00 -0.200000
2023-09-22 14:30:00+00:00 0.000003
2023-09-22 15:00:00+00:00 -0.700003
[17889 rows x 1 columns]
ADF Statistic: -21.690645317785734
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 3, 1) Log Likelihood -14439.608
Date: Sun, 24 Dec 2023 AIC 28943.217
Time: 20:43:19 BIC 29192.556
Sample: 0 HQIC 29025.236
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.9058 0.003 -349.971 0.000 -0.911 -0.901
ar.L2 -0.8825 0.004 -228.635 0.000 -0.890 -0.875
ar.L3 -0.8595 0.006 -155.683 0.000 -0.870 -0.849
ar.L4 -0.8291 0.007 -124.146 0.000 -0.842 -0.816
ar.L5 -0.7964 0.007 -107.091 0.000 -0.811 -0.782
ar.L6 -0.7438 0.008 -89.619 0.000 -0.760 -0.728
ar.L7 -0.7058 0.009 -79.848 0.000 -0.723 -0.689
ar.L8 -0.6878 0.009 -73.613 0.000 -0.706 -0.669
ar.L9 -0.6329 0.010 -64.710 0.000 -0.652 -0.614
ar.L10 -0.5810 0.010 -60.146 0.000 -0.600 -0.562
ar.L11 -0.5507 0.010 -56.829 0.000 -0.570 -0.532
ar.L12 -0.5207 0.010 -53.006 0.000 -0.540 -0.501
ar.L13 -0.4688 0.010 -47.221 0.000 -0.488 -0.449
ar.L14 -0.4295 0.010 -42.458 0.000 -0.449 -0.410
ar.L15 -0.4145 0.010 -40.207 0.000 -0.435 -0.394
ar.L16 -0.3792 0.010 -37.380 0.000 -0.399 -0.359
ar.L17 -0.3290 0.010 -32.460 0.000 -0.349 -0.309
ar.L18 -0.3042 0.010 -30.748 0.000 -0.324 -0.285
ar.L19 -0.2996 0.010 -30.723 0.000 -0.319 -0.280
ar.L20 -0.3101 0.010 -32.102 0.000 -0.329 -0.291
ar.L21 -0.3150 0.010 -31.742 0.000 -0.334 -0.296
ar.L22 -0.3093 0.009 -33.433 0.000 -0.327 -0.291
ar.L23 -0.2874 0.009 -30.584 0.000 -0.306 -0.269
ar.L24 -0.2786 0.009 -30.334 0.000 -0.297 -0.261
ar.L25 -0.2198 0.008 -26.032 0.000 -0.236 -0.203
ar.L26 -0.1508 0.008 -18.147 0.000 -0.167 -0.134
ar.L27 -0.1178 0.008 -15.554 0.000 -0.133 -0.103
ar.L28 -0.0911 0.007 -13.582 0.000 -0.104 -0.078
ar.L29 -0.0556 0.006 -9.915 0.000 -0.067 -0.045
ar.L30 -0.0308 0.004 -7.906 0.000 -0.038 -0.023
ma.L1 -1.0000 0.004 -275.306 0.000 -1.007 -0.993
sigma2 0.2940 0.001 290.720 0.000 0.292 0.296
===================================================================================
Ljung-Box (L1) (Q): 0.02 Jarque-Bera (JB): 4171612.78
Prob(Q): 0.89 Prob(JB): 0.00
Heteroskedasticity (H): 58.08 Skew: 3.00
Prob(H) (two-sided): 0.00 Kurtosis: 77.57
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 226.131018
17891 226.268528
17892 226.420330
17893 226.608795
17894 226.707874
17895 226.591448
17896 226.554271
17897 226.543665
17898 226.419615
17899 226.231836
Name: predicted_mean, dtype: float64
lower price upper price
17890 225.068295 227.193740
17891 224.693158 227.843899
17892 224.441683 228.398977
17893 224.280469 228.937120
17894 224.057825 229.357923
17895 223.635965 229.546930
17896 223.294828 229.813714
17897 222.984245 230.103086
17898 222.570495 230.268736
17899 222.087304 230.376369
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 3.9116086638429937 Weighted Mean Absolute Percentage Error (WMAPE): 1.4844957820835403
forecastplusyahoo('AKBNK', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 6.6937 AKBNK
2021-12-27 10:00:00+03:00 6.7991 AKBNK
2021-12-27 11:00:00+03:00 6.8343 AKBNK
2021-12-27 12:00:00+03:00 6.8430 AKBNK
2021-12-27 13:00:00+03:00 6.8255 AKBNK
... ... ...
2023-09-22 14:00:00+03:00 31.0800 AKBNK
2023-09-22 15:00:00+03:00 30.9000 AKBNK
2023-09-22 16:00:00+03:00 31.1400 AKBNK
2023-09-22 17:00:00+03:00 30.8400 AKBNK
2023-09-22 18:00:00+03:00 30.8000 AKBNK
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for AKBNK.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 6.6937
2021-12-27 06:30:00+00:00 7.6900
2021-12-27 07:00:00+00:00 6.7991
2021-12-27 07:30:00+00:00 7.7600
2021-12-27 08:00:00+00:00 6.8343
2021-12-27 08:30:00+00:00 7.8100
2021-12-27 09:00:00+00:00 6.8430
2021-12-27 09:30:00+00:00 7.7700
2021-12-27 10:00:00+00:00 6.8255
2021-12-27 10:30:00+00:00 7.7900
2021-12-27 11:00:00+00:00 6.7816
2021-12-27 11:30:00+00:00 7.6800
2021-12-27 12:00:00+00:00 6.7904
2021-12-27 12:30:00+00:00 7.7700
2021-12-27 13:00:00+00:00 6.8343
2021-12-27 13:30:00+00:00 7.8200
2021-12-27 14:00:00+00:00 6.8782
2021-12-27 15:00:00+00:00 6.8782
2021-12-28 06:00:00+00:00 6.8430
2021-12-28 06:30:00+00:00 7.6100
2021-12-28 07:00:00+00:00 6.6850
2021-12-28 07:30:00+00:00 7.5900
2021-12-28 08:00:00+00:00 6.6323
2021-12-28 08:30:00+00:00 7.5300
2021-12-28 09:00:00+00:00 6.6147
2021-12-28 09:30:00+00:00 7.5000
2021-12-28 10:00:00+00:00 6.5884
2021-12-28 10:30:00+00:00 7.5200
2021-12-28 11:00:00+00:00 6.5884
2021-12-28 11:30:00+00:00 7.5100
2021-12-28 12:00:00+00:00 6.6410
2021-12-28 12:30:00+00:00 7.5200
2021-12-28 13:00:00+00:00 6.5708
2021-12-28 13:30:00+00:00 7.4000
2021-12-28 14:00:00+00:00 6.4917
2021-12-28 15:00:00+00:00 6.4653
2021-12-29 06:00:00+00:00 6.3775
2021-12-29 06:30:00+00:00 7.2000
2021-12-29 07:00:00+00:00 6.3599
2021-12-29 07:30:00+00:00 7.3300
2021-12-29 08:00:00+00:00 6.4477
2021-12-29 08:30:00+00:00 7.3300
2021-12-29 09:00:00+00:00 6.4389
2021-12-29 09:30:00+00:00 7.3300
2021-12-29 10:00:00+00:00 6.4389
2021-12-29 10:30:00+00:00 7.2900
2021-12-29 11:00:00+00:00 6.4214
2021-12-29 11:30:00+00:00 7.3600
2021-12-29 12:00:00+00:00 6.4741
2021-12-29 12:30:00+00:00 7.4100
price
2023-09-21 10:00:00+00:00 30.680000
2023-09-21 10:30:00+00:00 29.840000
2023-09-21 11:00:00+00:00 30.180000
2023-09-21 11:30:00+00:00 30.059999
2023-09-21 12:00:00+00:00 30.160000
2023-09-21 12:30:00+00:00 30.580000
2023-09-21 13:00:00+00:00 30.660000
2023-09-21 13:30:00+00:00 30.680000
2023-09-21 14:00:00+00:00 31.240000
2023-09-21 14:30:00+00:00 31.240000
2023-09-21 15:00:00+00:00 31.200000
2023-09-22 06:00:00+00:00 31.200000
2023-09-22 06:30:00+00:00 31.139999
2023-09-22 07:00:00+00:00 31.080000
2023-09-22 07:30:00+00:00 31.379999
2023-09-22 08:00:00+00:00 31.260000
2023-09-22 08:30:00+00:00 31.200001
2023-09-22 09:00:00+00:00 31.200000
2023-09-22 09:30:00+00:00 31.379999
2023-09-22 10:00:00+00:00 31.440000
2023-09-22 10:30:00+00:00 31.280001
2023-09-22 11:00:00+00:00 31.080000
2023-09-22 11:30:00+00:00 30.900000
2023-09-22 12:00:00+00:00 30.900000
2023-09-22 12:30:00+00:00 31.340000
2023-09-22 13:00:00+00:00 31.140000
2023-09-22 13:30:00+00:00 31.100000
2023-09-22 14:00:00+00:00 30.840000
2023-09-22 14:30:00+00:00 30.840000
2023-09-22 15:00:00+00:00 30.800000
Mean of the first 10 values: price 31.734
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 30.94
2023-09-25 10:00:00+03:00 31.20
2023-09-25 11:00:00+03:00 31.32
2023-09-25 12:00:00+03:00 31.74
2023-09-25 13:00:00+03:00 31.98
2023-09-25 14:00:00+03:00 31.58
2023-09-25 15:00:00+03:00 31.78
2023-09-25 16:00:00+03:00 32.30
2023-09-25 17:00:00+03:00 32.28
2023-09-25 18:00:00+03:00 32.22
price
2018-01-02 10:00:00+03:00 1.127000e-01
2018-01-02 11:00:00+03:00 3.520000e-02
2018-01-02 12:00:00+03:00 -1.400000e-02
2018-01-02 13:00:00+03:00 2.100000e-02
2018-01-02 14:00:00+03:00 3.530000e-02
... ...
2023-09-22 13:00:00+00:00 -2.000002e-01
2023-09-22 13:30:00+00:00 -3.999962e-02
2023-09-22 14:00:00+00:00 -2.600004e-01
2023-09-22 14:30:00+00:00 1.525879e-07
2023-09-22 15:00:00+00:00 -4.000015e-02
[17890 rows x 1 columns]
ADF Statistic: -19.89541921245133
p-value: 0.0
Critical Values: {'1%': -3.4307164410996687, '5%': -2.8617019530116066, '10%': -2.5668562033496514}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17891
Model: ARIMA(30, 1, 1) Log Likelihood 3158.424
Date: Sun, 24 Dec 2023 AIC -6252.847
Time: 20:46:03 BIC -6003.503
Sample: 0 HQIC -6170.827
- 17891
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.4546 0.236 -1.927 0.054 -0.917 0.008
ar.L2 0.2564 0.203 1.261 0.207 -0.142 0.655
ar.L3 -0.0086 0.023 -0.377 0.706 -0.053 0.036
ar.L4 0.0245 0.013 1.949 0.051 -0.000 0.049
ar.L5 -0.0301 0.006 -5.303 0.000 -0.041 -0.019
ar.L6 0.0351 0.009 4.053 0.000 0.018 0.052
ar.L7 -0.0321 0.009 -3.732 0.000 -0.049 -0.015
ar.L8 0.0177 0.009 2.061 0.039 0.001 0.035
ar.L9 -0.0271 0.007 -3.698 0.000 -0.042 -0.013
ar.L10 0.0426 0.009 4.844 0.000 0.025 0.060
ar.L11 0.0134 0.009 1.441 0.150 -0.005 0.032
ar.L12 -0.0289 0.009 -3.332 0.001 -0.046 -0.012
ar.L13 -0.0228 0.007 -3.467 0.001 -0.036 -0.010
ar.L14 -0.0483 0.008 -5.816 0.000 -0.065 -0.032
ar.L15 -0.0154 0.015 -1.031 0.303 -0.045 0.014
ar.L16 -0.1679 0.010 -16.427 0.000 -0.188 -0.148
ar.L17 -0.0391 0.044 -0.894 0.371 -0.125 0.047
ar.L18 0.4337 0.027 15.932 0.000 0.380 0.487
ar.L19 0.3392 0.091 3.721 0.000 0.160 0.518
ar.L20 -0.2754 0.117 -2.352 0.019 -0.505 -0.046
ar.L21 -0.1264 0.018 -7.119 0.000 -0.161 -0.092
ar.L22 0.0053 0.037 0.142 0.887 -0.067 0.078
ar.L23 -0.0556 0.015 -3.800 0.000 -0.084 -0.027
ar.L24 0.0099 0.020 0.503 0.615 -0.029 0.049
ar.L25 -0.0301 0.008 -3.776 0.000 -0.046 -0.014
ar.L26 0.0297 0.012 2.573 0.010 0.007 0.052
ar.L27 -0.0293 0.008 -3.908 0.000 -0.044 -0.015
ar.L28 0.0073 0.009 0.805 0.421 -0.011 0.025
ar.L29 -0.0641 0.006 -10.511 0.000 -0.076 -0.052
ar.L30 0.0505 0.017 2.998 0.003 0.017 0.084
ma.L1 -0.4060 0.236 -1.722 0.085 -0.868 0.056
sigma2 0.0411 0.000 247.447 0.000 0.041 0.041
===================================================================================
Ljung-Box (L1) (Q): 0.15 Jarque-Bera (JB): 320415.70
Prob(Q): 0.70 Prob(JB): 0.00
Heteroskedasticity (H): 25.15 Skew: -0.71
Prob(H) (two-sided): 0.00 Kurtosis: 23.68
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891 30.809788
17892 30.636290
17893 30.884327
17894 30.754408
17895 30.750428
17896 30.682619
17897 30.872461
17898 30.822663
17899 30.871648
17900 30.649573
Name: predicted_mean, dtype: float64
lower price upper price
17891 30.412487 31.207090
17892 30.235147 31.037434
17893 30.375712 31.392942
17894 30.235143 31.273673
17895 30.161489 31.339367
17896 30.080930 31.284307
17897 30.212862 31.532060
17898 30.151382 31.493943
17899 30.149050 31.594246
17900 29.916656 31.382489
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 1.0615514510933883 Weighted Mean Absolute Percentage Error (WMAPE): 3.0269725281847215
forecastplusyahoo('ARCLK', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 49.8145 ARCLK
2021-12-27 10:00:00+03:00 49.6728 ARCLK
2021-12-27 11:00:00+03:00 49.4837 ARCLK
2021-12-27 12:00:00+03:00 48.9639 ARCLK
2021-12-27 13:00:00+03:00 49.3419 ARCLK
... ... ...
2023-09-22 14:00:00+03:00 155.2158 ARCLK
2023-09-22 15:00:00+03:00 155.0192 ARCLK
2023-09-22 16:00:00+03:00 155.5107 ARCLK
2023-09-22 17:00:00+03:00 153.3481 ARCLK
2023-09-22 18:00:00+03:00 154.0362 ARCLK
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for ARCLK.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 49.814500
2021-12-27 06:30:00+00:00 52.549999
2021-12-27 07:00:00+00:00 49.672800
2021-12-27 07:30:00+00:00 52.650002
2021-12-27 08:00:00+00:00 49.483700
2021-12-27 08:30:00+00:00 52.200001
2021-12-27 09:00:00+00:00 48.963900
2021-12-27 09:30:00+00:00 51.950001
2021-12-27 10:00:00+00:00 49.341900
2021-12-27 10:30:00+00:00 52.000000
2021-12-27 11:00:00+00:00 48.774700
2021-12-27 11:30:00+00:00 50.849998
2021-12-27 12:00:00+00:00 48.113100
2021-12-27 12:30:00+00:00 50.799999
2021-12-27 13:00:00+00:00 48.302200
2021-12-27 13:30:00+00:00 51.299999
2021-12-27 14:00:00+00:00 48.349400
2021-12-27 15:00:00+00:00 48.113100
2021-12-28 06:00:00+00:00 48.443900
2021-12-28 06:30:00+00:00 50.450001
2021-12-28 07:00:00+00:00 48.443900
2021-12-28 07:30:00+00:00 51.049999
2021-12-28 08:00:00+00:00 48.680300
2021-12-28 08:30:00+00:00 51.250000
2021-12-28 09:00:00+00:00 48.443900
2021-12-28 09:30:00+00:00 51.099998
2021-12-28 10:00:00+00:00 48.113100
2021-12-28 10:30:00+00:00 50.700001
2021-12-28 11:00:00+00:00 47.735000
2021-12-28 11:30:00+00:00 50.599998
2021-12-28 12:00:00+00:00 47.498700
2021-12-28 12:30:00+00:00 50.250000
2021-12-28 13:00:00+00:00 47.356900
2021-12-28 13:30:00+00:00 49.320000
2021-12-28 14:00:00+00:00 46.789800
2021-12-28 15:00:00+00:00 46.827600
2021-12-29 06:00:00+00:00 46.506200
2021-12-29 06:30:00+00:00 48.700001
2021-12-29 07:00:00+00:00 46.506200
2021-12-29 07:30:00+00:00 49.799999
2021-12-29 08:00:00+00:00 47.498700
2021-12-29 08:30:00+00:00 49.980000
2021-12-29 09:00:00+00:00 47.404100
2021-12-29 09:30:00+00:00 50.049999
2021-12-29 10:00:00+00:00 47.356900
2021-12-29 10:30:00+00:00 49.799999
2021-12-29 11:00:00+00:00 47.404100
2021-12-29 11:30:00+00:00 50.200001
2021-12-29 12:00:00+00:00 47.404100
2021-12-29 12:30:00+00:00 50.200001
price
2023-09-21 10:00:00+00:00 150.005900
2023-09-21 10:30:00+00:00 154.300003
2023-09-21 11:00:00+00:00 153.937900
2023-09-21 11:30:00+00:00 156.199997
2023-09-21 12:00:00+00:00 153.053200
2023-09-21 12:30:00+00:00 157.100006
2023-09-21 13:00:00+00:00 153.741300
2023-09-21 13:30:00+00:00 158.000000
2023-09-21 14:00:00+00:00 155.510700
2023-09-21 14:30:00+00:00 158.199997
2023-09-21 15:00:00+00:00 155.609000
2023-09-22 06:00:00+00:00 155.707300
2023-09-22 06:30:00+00:00 157.800003
2023-09-22 07:00:00+00:00 154.822600
2023-09-22 07:30:00+00:00 157.699997
2023-09-22 08:00:00+00:00 155.314100
2023-09-22 08:30:00+00:00 158.100006
2023-09-22 09:00:00+00:00 155.314100
2023-09-22 09:30:00+00:00 157.899994
2023-09-22 10:00:00+00:00 155.903900
2023-09-22 10:30:00+00:00 158.199997
2023-09-22 11:00:00+00:00 155.215800
2023-09-22 11:30:00+00:00 156.899994
2023-09-22 12:00:00+00:00 155.019200
2023-09-22 12:30:00+00:00 158.100006
2023-09-22 13:00:00+00:00 155.510700
2023-09-22 13:30:00+00:00 157.699997
2023-09-22 14:00:00+00:00 153.348100
2023-09-22 14:30:00+00:00 156.000000
2023-09-22 15:00:00+00:00 154.036200
Mean of the first 10 values: price 156.59
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 155.6
2023-09-25 10:00:00+03:00 155.5
2023-09-25 11:00:00+03:00 156.3
2023-09-25 12:00:00+03:00 156.3
2023-09-25 13:00:00+03:00 157.6
2023-09-25 14:00:00+03:00 156.4
2023-09-25 15:00:00+03:00 156.5
2023-09-25 16:00:00+03:00 157.3
2023-09-25 17:00:00+03:00 157.2
2023-09-25 18:00:00+03:00 157.2
price
2018-01-02 10:00:00+03:00 0.085300
2018-01-02 11:00:00+03:00 -0.119500
2018-01-02 12:00:00+03:00 -0.017100
2018-01-02 13:00:00+03:00 0.000000
2018-01-02 14:00:00+03:00 0.102500
... ...
2023-09-22 13:00:00+00:00 -2.589306
2023-09-22 13:30:00+00:00 2.189297
2023-09-22 14:00:00+00:00 -4.351897
2023-09-22 14:30:00+00:00 2.651900
2023-09-22 15:00:00+00:00 -1.963800
[17890 rows x 1 columns]
ADF Statistic: -19.33402242684771
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17891
Model: ARIMA(30, 1, 1) Log Likelihood -18280.318
Date: Sun, 24 Dec 2023 AIC 36624.636
Time: 20:48:49 BIC 36873.980
Sample: 0 HQIC 36706.656
- 17891
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.2942 0.005 -255.452 0.000 -1.304 -1.284
ar.L2 -0.3086 0.006 -55.444 0.000 -0.320 -0.298
ar.L3 0.0052 0.006 0.809 0.418 -0.007 0.018
ar.L4 -0.0296 0.006 -4.602 0.000 -0.042 -0.017
ar.L5 -0.0108 0.006 -1.656 0.098 -0.023 0.002
ar.L6 -0.0014 0.006 -0.223 0.823 -0.014 0.011
ar.L7 0.0121 0.007 1.861 0.063 -0.001 0.025
ar.L8 0.0255 0.007 3.728 0.000 0.012 0.039
ar.L9 0.0196 0.007 2.890 0.004 0.006 0.033
ar.L10 0.0107 0.007 1.567 0.117 -0.003 0.024
ar.L11 -0.0338 0.007 -4.893 0.000 -0.047 -0.020
ar.L12 -0.0394 0.007 -5.763 0.000 -0.053 -0.026
ar.L13 -0.0058 0.007 -0.815 0.415 -0.020 0.008
ar.L14 0.0122 0.007 1.707 0.088 -0.002 0.026
ar.L15 0.0310 0.007 4.675 0.000 0.018 0.044
ar.L16 -0.0504 0.007 -7.286 0.000 -0.064 -0.037
ar.L17 -0.0590 0.007 -8.985 0.000 -0.072 -0.046
ar.L18 0.0900 0.006 14.804 0.000 0.078 0.102
ar.L19 0.2844 0.005 53.702 0.000 0.274 0.295
ar.L20 0.0502 0.005 9.181 0.000 0.040 0.061
ar.L21 -0.1591 0.006 -24.995 0.000 -0.172 -0.147
ar.L22 -0.0487 0.007 -6.740 0.000 -0.063 -0.035
ar.L23 -0.0359 0.007 -5.131 0.000 -0.050 -0.022
ar.L24 -0.0159 0.007 -2.292 0.022 -0.029 -0.002
ar.L25 -0.0188 0.007 -2.707 0.007 -0.032 -0.005
ar.L26 -0.0090 0.007 -1.299 0.194 -0.023 0.005
ar.L27 -0.0245 0.007 -3.366 0.001 -0.039 -0.010
ar.L28 -0.0302 0.007 -4.188 0.000 -0.044 -0.016
ar.L29 0.0312 0.007 4.430 0.000 0.017 0.045
ar.L30 0.0830 0.005 16.656 0.000 0.073 0.093
ma.L1 0.8918 0.004 230.359 0.000 0.884 0.899
sigma2 0.4528 0.002 257.954 0.000 0.449 0.456
===================================================================================
Ljung-Box (L1) (Q): 1.79 Jarque-Bera (JB): 214042.73
Prob(Q): 0.18 Prob(JB): 0.00
Heteroskedasticity (H): 78.28 Skew: 0.92
Prob(H) (two-sided): 0.00 Kurtosis: 19.85
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891 155.064428
17892 155.457883
17893 154.139143
17894 156.070727
17895 153.926707
17896 156.178491
17897 153.636560
17898 156.449453
17899 153.923436
17900 156.393710
Name: predicted_mean, dtype: float64
lower price upper price
17891 153.745593 156.383263
17892 153.921474 156.994291
17893 152.268029 156.010257
17894 154.004500 158.136954
17895 151.632907 156.220507
17896 153.713212 158.643770
17897 150.983268 156.289851
17898 153.638159 159.260747
17899 150.940554 156.906318
17900 153.265284 159.522136
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 1.9713275245319117 Weighted Mean Absolute Percentage Error (WMAPE): 1.0168957271982435
forecastplusyahoo('ASELS', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 10.9361 ASELS
2021-12-27 10:00:00+03:00 11.2048 ASELS
2021-12-27 11:00:00+03:00 11.1252 ASELS
2021-12-27 12:00:00+03:00 11.0655 ASELS
2021-12-27 13:00:00+03:00 11.0754 ASELS
... ... ...
2023-09-22 14:00:00+03:00 40.3000 ASELS
2023-09-22 15:00:00+03:00 40.1600 ASELS
2023-09-22 16:00:00+03:00 40.2400 ASELS
2023-09-22 17:00:00+03:00 40.6400 ASELS
2023-09-22 18:00:00+03:00 40.5200 ASELS
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for ASELS.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 10.9361
2021-12-27 06:30:00+00:00 11.1700
2021-12-27 07:00:00+00:00 11.2048
2021-12-27 07:30:00+00:00 11.1500
2021-12-27 08:00:00+00:00 11.1252
2021-12-27 08:30:00+00:00 11.1400
2021-12-27 09:00:00+00:00 11.0655
2021-12-27 09:30:00+00:00 11.0800
2021-12-27 10:00:00+00:00 11.0754
2021-12-27 10:30:00+00:00 11.1000
2021-12-27 11:00:00+00:00 10.9660
2021-12-27 11:30:00+00:00 10.8600
2021-12-27 12:00:00+00:00 10.9063
2021-12-27 12:30:00+00:00 10.9200
2021-12-27 13:00:00+00:00 10.8565
2021-12-27 13:30:00+00:00 11.0300
2021-12-27 14:00:00+00:00 10.8466
2021-12-27 15:00:00+00:00 10.8565
2021-12-28 06:00:00+00:00 10.9560
2021-12-28 06:30:00+00:00 10.9700
2021-12-28 07:00:00+00:00 10.9759
2021-12-28 07:30:00+00:00 10.9600
2021-12-28 08:00:00+00:00 10.8266
2021-12-28 08:30:00+00:00 10.8400
2021-12-28 09:00:00+00:00 10.7570
2021-12-28 09:30:00+00:00 10.8100
2021-12-28 10:00:00+00:00 10.7172
2021-12-28 10:30:00+00:00 10.7500
2021-12-28 11:00:00+00:00 10.6376
2021-12-28 11:30:00+00:00 10.7900
2021-12-28 12:00:00+00:00 10.7172
2021-12-28 12:30:00+00:00 10.7500
2021-12-28 13:00:00+00:00 10.6177
2021-12-28 13:30:00+00:00 10.4600
2021-12-28 14:00:00+00:00 10.4983
2021-12-28 15:00:00+00:00 10.5281
2021-12-29 06:00:00+00:00 10.4485
2021-12-29 06:30:00+00:00 10.3400
2021-12-29 07:00:00+00:00 10.3590
2021-12-29 07:30:00+00:00 10.5300
2021-12-29 08:00:00+00:00 10.6077
2021-12-29 08:30:00+00:00 10.6900
2021-12-29 09:00:00+00:00 10.7072
2021-12-29 09:30:00+00:00 10.7100
2021-12-29 10:00:00+00:00 10.7072
2021-12-29 10:30:00+00:00 10.7000
2021-12-29 11:00:00+00:00 10.7371
2021-12-29 11:30:00+00:00 10.8200
2021-12-29 12:00:00+00:00 10.7570
2021-12-29 12:30:00+00:00 10.8800
price
2023-09-21 10:00:00+00:00 37.980000
2023-09-21 10:30:00+00:00 38.540001
2023-09-21 11:00:00+00:00 39.020000
2023-09-21 11:30:00+00:00 38.939999
2023-09-21 12:00:00+00:00 38.980000
2023-09-21 12:30:00+00:00 39.299999
2023-09-21 13:00:00+00:00 39.240000
2023-09-21 13:30:00+00:00 39.599998
2023-09-21 14:00:00+00:00 39.680000
2023-09-21 14:30:00+00:00 39.680000
2023-09-21 15:00:00+00:00 39.860000
2023-09-22 06:00:00+00:00 39.860000
2023-09-22 06:30:00+00:00 40.099998
2023-09-22 07:00:00+00:00 40.940000
2023-09-22 07:30:00+00:00 40.779999
2023-09-22 08:00:00+00:00 40.780000
2023-09-22 08:30:00+00:00 40.700001
2023-09-22 09:00:00+00:00 40.660000
2023-09-22 09:30:00+00:00 40.580002
2023-09-22 10:00:00+00:00 40.520000
2023-09-22 10:30:00+00:00 40.540001
2023-09-22 11:00:00+00:00 40.300000
2023-09-22 11:30:00+00:00 40.080002
2023-09-22 12:00:00+00:00 40.160000
2023-09-22 12:30:00+00:00 40.340000
2023-09-22 13:00:00+00:00 40.240000
2023-09-22 13:30:00+00:00 40.180000
2023-09-22 14:00:00+00:00 40.640000
2023-09-22 14:30:00+00:00 40.639999
2023-09-22 15:00:00+00:00 40.520000
Mean of the first 10 values: price 41.686
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 40.62
2023-09-25 10:00:00+03:00 41.54
2023-09-25 11:00:00+03:00 41.82
2023-09-25 12:00:00+03:00 41.54
2023-09-25 13:00:00+03:00 41.88
2023-09-25 14:00:00+03:00 41.62
2023-09-25 15:00:00+03:00 41.56
2023-09-25 16:00:00+03:00 41.66
2023-09-25 17:00:00+03:00 42.32
2023-09-25 18:00:00+03:00 42.30
price
2018-01-02 10:00:00+03:00 1.440000e-02
2018-01-02 11:00:00+03:00 -1.440000e-02
2018-01-02 12:00:00+03:00 2.890000e-02
2018-01-02 13:00:00+03:00 4.800000e-03
2018-01-02 14:00:00+03:00 -9.600000e-03
... ...
2023-09-22 13:00:00+00:00 -1.000002e-01
2023-09-22 13:30:00+00:00 -5.999969e-02
2023-09-22 14:00:00+00:00 4.599997e-01
2023-09-22 14:30:00+00:00 -6.103516e-07
2023-09-22 15:00:00+00:00 -1.199994e-01
[17780 rows x 1 columns]
ADF Statistic: -20.09626406294903
p-value: 0.0
Critical Values: {'1%': -3.430718776247112, '5%': -2.861702984993756, '10%': -2.5668567526578725}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17781
Model: ARIMA(30, 3, 1) Log Likelihood 11009.200
Date: Sun, 24 Dec 2023 AIC -21954.399
Time: 20:52:13 BIC -21705.256
Sample: 0 HQIC -21872.420
- 17781
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.9971 0.003 -291.484 0.000 -1.004 -0.990
ar.L2 -0.9346 0.005 -191.695 0.000 -0.944 -0.925
ar.L3 -0.9495 0.006 -146.890 0.000 -0.962 -0.937
ar.L4 -0.8774 0.008 -112.083 0.000 -0.893 -0.862
ar.L5 -0.8668 0.009 -95.139 0.000 -0.885 -0.849
ar.L6 -0.8082 0.010 -78.064 0.000 -0.828 -0.788
ar.L7 -0.7945 0.011 -72.250 0.000 -0.816 -0.773
ar.L8 -0.7365 0.011 -65.281 0.000 -0.759 -0.714
ar.L9 -0.7342 0.012 -62.525 0.000 -0.757 -0.711
ar.L10 -0.6724 0.012 -54.960 0.000 -0.696 -0.648
ar.L11 -0.6179 0.013 -48.928 0.000 -0.643 -0.593
ar.L12 -0.5912 0.012 -48.230 0.000 -0.615 -0.567
ar.L13 -0.5473 0.013 -42.800 0.000 -0.572 -0.522
ar.L14 -0.5137 0.013 -39.469 0.000 -0.539 -0.488
ar.L15 -0.4846 0.013 -37.591 0.000 -0.510 -0.459
ar.L16 -0.4608 0.013 -35.441 0.000 -0.486 -0.435
ar.L17 -0.3988 0.013 -31.417 0.000 -0.424 -0.374
ar.L18 -0.3795 0.013 -30.094 0.000 -0.404 -0.355
ar.L19 -0.3448 0.012 -28.045 0.000 -0.369 -0.321
ar.L20 -0.3468 0.012 -28.926 0.000 -0.370 -0.323
ar.L21 -0.3747 0.012 -31.728 0.000 -0.398 -0.352
ar.L22 -0.3541 0.012 -29.613 0.000 -0.378 -0.331
ar.L23 -0.3126 0.012 -26.375 0.000 -0.336 -0.289
ar.L24 -0.2677 0.012 -22.594 0.000 -0.291 -0.244
ar.L25 -0.2484 0.011 -22.975 0.000 -0.270 -0.227
ar.L26 -0.1837 0.010 -18.230 0.000 -0.203 -0.164
ar.L27 -0.1822 0.009 -19.843 0.000 -0.200 -0.164
ar.L28 -0.1286 0.008 -15.894 0.000 -0.144 -0.113
ar.L29 -0.0979 0.007 -14.841 0.000 -0.111 -0.085
ar.L30 -0.0391 0.005 -8.596 0.000 -0.048 -0.030
ma.L1 -0.9983 0.001 -671.315 0.000 -1.001 -0.995
sigma2 0.0170 4.8e-05 355.057 0.000 0.017 0.017
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 1731880.26
Prob(Q): 0.91 Prob(JB): 0.00
Heteroskedasticity (H): 19.63 Skew: 1.75
Prob(H) (two-sided): 0.00 Kurtosis: 51.23
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17781 40.620932
17782 40.720806
17783 40.792644
17784 40.838651
17785 40.844985
17786 40.927508
17787 40.997250
17788 41.085788
17789 41.160636
17790 41.229119
Name: predicted_mean, dtype: float64
lower price upper price
17781 40.365083 40.876780
17782 40.358137 41.083476
17783 40.338429 41.246858
17784 40.310140 41.367161
17785 40.242093 41.447876
17786 40.257209 41.597807
17787 40.258364 41.736135
17788 40.282652 41.888925
17789 40.291003 42.030269
17790 40.297158 42.161080
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.8310566241125393 Weighted Mean Absolute Percentage Error (WMAPE): 1.9779651702869407
forecastplusyahoo('BIMAS', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 62.9265 BIMAS
2021-12-27 10:00:00+03:00 63.4541 BIMAS
2021-12-27 11:00:00+03:00 62.8305 BIMAS
2021-12-27 12:00:00+03:00 62.5908 BIMAS
2021-12-27 13:00:00+03:00 62.4948 BIMAS
... ... ...
2023-09-22 14:00:00+03:00 274.0000 BIMAS
2023-09-22 15:00:00+03:00 273.0000 BIMAS
2023-09-22 16:00:00+03:00 272.4000 BIMAS
2023-09-22 17:00:00+03:00 273.4000 BIMAS
2023-09-22 18:00:00+03:00 273.4000 BIMAS
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for BIMAS.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 62.926500
2021-12-27 06:30:00+00:00 65.800003
2021-12-27 07:00:00+00:00 63.454100
2021-12-27 07:30:00+00:00 65.550003
2021-12-27 08:00:00+00:00 62.830500
2021-12-27 08:30:00+00:00 65.449997
2021-12-27 09:00:00+00:00 62.590800
2021-12-27 09:30:00+00:00 65.000000
2021-12-27 10:00:00+00:00 62.494800
2021-12-27 10:30:00+00:00 65.000000
2021-12-27 11:00:00+00:00 61.775300
2021-12-27 11:30:00+00:00 64.150002
2021-12-27 12:00:00+00:00 61.775300
2021-12-27 12:30:00+00:00 64.349998
2021-12-27 13:00:00+00:00 61.727400
2021-12-27 13:30:00+00:00 64.750000
2021-12-27 14:00:00+00:00 61.439600
2021-12-27 15:00:00+00:00 61.295700
2021-12-28 06:00:00+00:00 61.871400
2021-12-28 06:30:00+00:00 64.599998
2021-12-28 07:00:00+00:00 61.823300
2021-12-28 07:30:00+00:00 64.199997
2021-12-28 08:00:00+00:00 61.391600
2021-12-28 08:30:00+00:00 63.650002
2021-12-28 09:00:00+00:00 61.008000
2021-12-28 09:30:00+00:00 63.799999
2021-12-28 10:00:00+00:00 61.151900
2021-12-28 10:30:00+00:00 63.450001
2021-12-28 11:00:00+00:00 60.720200
2021-12-28 11:30:00+00:00 63.900002
2021-12-28 12:00:00+00:00 61.247800
2021-12-28 12:30:00+00:00 63.750000
2021-12-28 13:00:00+00:00 60.624300
2021-12-28 13:30:00+00:00 62.500000
2021-12-28 14:00:00+00:00 60.000700
2021-12-28 15:00:00+00:00 60.048800
2021-12-29 06:00:00+00:00 60.096700
2021-12-29 06:30:00+00:00 62.849998
2021-12-29 07:00:00+00:00 60.576400
2021-12-29 07:30:00+00:00 63.650002
2021-12-29 08:00:00+00:00 61.391600
2021-12-29 08:30:00+00:00 63.900002
2021-12-29 09:00:00+00:00 61.439600
2021-12-29 09:30:00+00:00 64.050003
2021-12-29 10:00:00+00:00 61.439600
2021-12-29 10:30:00+00:00 63.799999
2021-12-29 11:00:00+00:00 61.343700
2021-12-29 11:30:00+00:00 64.199997
2021-12-29 12:00:00+00:00 61.343700
2021-12-29 12:30:00+00:00 63.950001
price
2023-09-21 10:00:00+00:00 270.600000
2023-09-21 10:30:00+00:00 273.200012
2023-09-21 11:00:00+00:00 276.000000
2023-09-21 11:30:00+00:00 275.100006
2023-09-21 12:00:00+00:00 273.800000
2023-09-21 12:30:00+00:00 275.600006
2023-09-21 13:00:00+00:00 274.300000
2023-09-21 13:30:00+00:00 274.200012
2023-09-21 14:00:00+00:00 276.000000
2023-09-21 14:30:00+00:00 276.000000
2023-09-21 15:00:00+00:00 275.700000
2023-09-22 06:00:00+00:00 278.000000
2023-09-22 06:30:00+00:00 277.100006
2023-09-22 07:00:00+00:00 274.100000
2023-09-22 07:30:00+00:00 274.200012
2023-09-22 08:00:00+00:00 274.300000
2023-09-22 08:30:00+00:00 275.399994
2023-09-22 09:00:00+00:00 276.200000
2023-09-22 09:30:00+00:00 274.600006
2023-09-22 10:00:00+00:00 275.400000
2023-09-22 10:30:00+00:00 274.500000
2023-09-22 11:00:00+00:00 274.000000
2023-09-22 11:30:00+00:00 272.600006
2023-09-22 12:00:00+00:00 273.000000
2023-09-22 12:30:00+00:00 272.600006
2023-09-22 13:00:00+00:00 272.400000
2023-09-22 13:30:00+00:00 271.799988
2023-09-22 14:00:00+00:00 273.400000
2023-09-22 14:30:00+00:00 273.399994
2023-09-22 15:00:00+00:00 273.400000
Mean of the first 10 values: price 274.68
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 275.0
2023-09-25 10:00:00+03:00 272.1
2023-09-25 11:00:00+03:00 272.9
2023-09-25 12:00:00+03:00 273.9
2023-09-25 13:00:00+03:00 274.2
2023-09-25 14:00:00+03:00 273.9
2023-09-25 15:00:00+03:00 273.7
2023-09-25 16:00:00+03:00 274.8
2023-09-25 17:00:00+03:00 278.6
2023-09-25 18:00:00+03:00 277.7
price
2018-01-02 10:00:00+03:00 0.346700
2018-01-02 11:00:00+03:00 -0.122600
2018-01-02 12:00:00+03:00 -0.183300
2018-01-02 13:00:00+03:00 0.142600
2018-01-02 14:00:00+03:00 0.142800
... ...
2023-09-22 13:00:00+00:00 -0.200006
2023-09-22 13:30:00+00:00 -0.600012
2023-09-22 14:00:00+00:00 1.600012
2023-09-22 14:30:00+00:00 -0.000006
2023-09-22 15:00:00+00:00 0.000006
[17889 rows x 1 columns]
ADF Statistic: -19.96823456823633
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 1, 1) Log Likelihood -22483.181
Date: Sun, 24 Dec 2023 AIC 45030.362
Time: 20:54:51 BIC 45279.704
Sample: 0 HQIC 45112.382
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.2601 0.006 -203.316 0.000 -1.272 -1.248
ar.L2 -0.2749 0.006 -48.309 0.000 -0.286 -0.264
ar.L3 -0.0154 0.007 -2.232 0.026 -0.029 -0.002
ar.L4 -0.0441 0.008 -5.527 0.000 -0.060 -0.028
ar.L5 -0.0144 0.008 -1.783 0.075 -0.030 0.001
ar.L6 0.0207 0.007 2.811 0.005 0.006 0.035
ar.L7 0.0443 0.007 6.601 0.000 0.031 0.057
ar.L8 0.0207 0.007 2.846 0.004 0.006 0.035
ar.L9 0.0217 0.007 2.953 0.003 0.007 0.036
ar.L10 0.0483 0.008 6.321 0.000 0.033 0.063
ar.L11 0.0511 0.008 6.633 0.000 0.036 0.066
ar.L12 0.0208 0.007 2.848 0.004 0.006 0.035
ar.L13 0.0213 0.007 3.174 0.002 0.008 0.035
ar.L14 0.0303 0.008 4.020 0.000 0.016 0.045
ar.L15 0.0460 0.008 5.837 0.000 0.031 0.061
ar.L16 -0.0346 0.008 -4.488 0.000 -0.050 -0.019
ar.L17 -0.0903 0.007 -12.344 0.000 -0.105 -0.076
ar.L18 0.0817 0.007 12.544 0.000 0.069 0.094
ar.L19 0.2501 0.005 54.299 0.000 0.241 0.259
ar.L20 -5.643e-05 0.005 -0.011 0.991 -0.010 0.010
ar.L21 -0.1588 0.006 -25.732 0.000 -0.171 -0.147
ar.L22 -0.0542 0.007 -7.555 0.000 -0.068 -0.040
ar.L23 -0.0534 0.008 -6.545 0.000 -0.069 -0.037
ar.L24 -0.0457 0.008 -5.642 0.000 -0.062 -0.030
ar.L25 -0.0326 0.008 -4.069 0.000 -0.048 -0.017
ar.L26 -0.0089 0.009 -1.043 0.297 -0.026 0.008
ar.L27 -0.0175 0.008 -2.183 0.029 -0.033 -0.002
ar.L28 -0.0428 0.008 -5.170 0.000 -0.059 -0.027
ar.L29 -0.0166 0.009 -1.942 0.052 -0.033 0.000
ar.L30 0.0550 0.006 9.161 0.000 0.043 0.067
ma.L1 0.8736 0.005 169.905 0.000 0.864 0.884
sigma2 0.7211 0.003 271.869 0.000 0.716 0.726
===================================================================================
Ljung-Box (L1) (Q): 0.21 Jarque-Bera (JB): 877251.61
Prob(Q): 0.65 Prob(JB): 0.00
Heteroskedasticity (H): 23.85 Skew: -0.10
Prob(H) (two-sided): 0.00 Kurtosis: 37.31
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 273.547564
17891 272.900536
17892 272.512197
17893 272.840211
17894 273.016342
17895 273.298569
17896 272.838092
17897 272.998038
17898 273.144972
17899 272.822982
Name: predicted_mean, dtype: float64
lower price upper price
17890 271.883226 275.211902
17891 270.947918 274.853153
17892 270.124526 274.899868
17893 270.219428 275.460993
17894 270.095265 275.937418
17895 270.175240 276.421898
17896 269.449960 276.226225
17897 269.414434 276.581642
17898 269.331115 276.958829
17899 268.823025 276.822938
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 2.516333261049285 Weighted Mean Absolute Percentage Error (WMAPE): 0.6904617330843175
forecastplusyahoo('DOHOL', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 2.6366 DOHOL
2021-12-27 10:00:00+03:00 2.6736 DOHOL
2021-12-27 11:00:00+03:00 2.6366 DOHOL
2021-12-27 12:00:00+03:00 2.6458 DOHOL
2021-12-27 13:00:00+03:00 2.6458 DOHOL
... ... ...
2023-09-22 14:00:00+03:00 13.0500 DOHOL
2023-09-22 15:00:00+03:00 13.0600 DOHOL
2023-09-22 16:00:00+03:00 13.0700 DOHOL
2023-09-22 17:00:00+03:00 13.0500 DOHOL
2023-09-22 18:00:00+03:00 13.0600 DOHOL
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for DOHOL.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 2.6366
2021-12-27 06:30:00+00:00 2.8500
2021-12-27 07:00:00+00:00 2.6736
2021-12-27 07:30:00+00:00 2.8500
2021-12-27 08:00:00+00:00 2.6366
2021-12-27 08:30:00+00:00 2.8500
2021-12-27 09:00:00+00:00 2.6458
2021-12-27 09:30:00+00:00 2.8400
2021-12-27 10:00:00+00:00 2.6458
2021-12-27 10:30:00+00:00 2.8500
2021-12-27 11:00:00+00:00 2.6180
2021-12-27 11:30:00+00:00 2.7900
2021-12-27 12:00:00+00:00 2.6086
2021-12-27 12:30:00+00:00 2.7900
2021-12-27 13:00:00+00:00 2.6086
2021-12-27 13:30:00+00:00 2.8100
2021-12-27 14:00:00+00:00 2.5901
2021-12-27 15:00:00+00:00 2.5809
2021-12-28 06:00:00+00:00 2.6086
2021-12-28 06:30:00+00:00 2.8100
2021-12-28 07:00:00+00:00 2.6366
2021-12-28 07:30:00+00:00 2.8100
2021-12-28 08:00:00+00:00 2.6086
2021-12-28 08:30:00+00:00 2.8000
2021-12-28 09:00:00+00:00 2.5901
2021-12-28 09:30:00+00:00 2.8000
2021-12-28 10:00:00+00:00 2.6086
2021-12-28 10:30:00+00:00 2.7900
2021-12-28 11:00:00+00:00 2.5901
2021-12-28 11:30:00+00:00 2.7900
2021-12-28 12:00:00+00:00 2.5901
2021-12-28 12:30:00+00:00 2.7800
2021-12-28 13:00:00+00:00 2.5715
2021-12-28 13:30:00+00:00 2.7300
2021-12-28 14:00:00+00:00 2.5529
2021-12-28 15:00:00+00:00 2.5437
2021-12-29 06:00:00+00:00 2.5622
2021-12-29 06:30:00+00:00 2.7600
2021-12-29 07:00:00+00:00 2.5529
2021-12-29 07:30:00+00:00 2.7700
2021-12-29 08:00:00+00:00 2.5809
2021-12-29 08:30:00+00:00 2.7700
2021-12-29 09:00:00+00:00 2.5901
2021-12-29 09:30:00+00:00 2.7900
2021-12-29 10:00:00+00:00 2.5901
2021-12-29 10:30:00+00:00 2.7800
2021-12-29 11:00:00+00:00 2.5901
2021-12-29 11:30:00+00:00 2.8000
2021-12-29 12:00:00+00:00 2.5901
2021-12-29 12:30:00+00:00 2.8200
price
2023-09-21 10:00:00+00:00 12.87
2023-09-21 10:30:00+00:00 12.95
2023-09-21 11:00:00+00:00 13.03
2023-09-21 11:30:00+00:00 13.00
2023-09-21 12:00:00+00:00 13.00
2023-09-21 12:30:00+00:00 13.13
2023-09-21 13:00:00+00:00 13.09
2023-09-21 13:30:00+00:00 13.28
2023-09-21 14:00:00+00:00 13.28
2023-09-21 14:30:00+00:00 13.28
2023-09-21 15:00:00+00:00 13.28
2023-09-22 06:00:00+00:00 13.28
2023-09-22 06:30:00+00:00 13.28
2023-09-22 07:00:00+00:00 13.25
2023-09-22 07:30:00+00:00 13.31
2023-09-22 08:00:00+00:00 13.32
2023-09-22 08:30:00+00:00 13.31
2023-09-22 09:00:00+00:00 13.33
2023-09-22 09:30:00+00:00 13.28
2023-09-22 10:00:00+00:00 13.32
2023-09-22 10:30:00+00:00 13.04
2023-09-22 11:00:00+00:00 13.05
2023-09-22 11:30:00+00:00 13.00
2023-09-22 12:00:00+00:00 13.06
2023-09-22 12:30:00+00:00 13.09
2023-09-22 13:00:00+00:00 13.07
2023-09-22 13:30:00+00:00 13.03
2023-09-22 14:00:00+00:00 13.05
2023-09-22 14:30:00+00:00 13.05
2023-09-22 15:00:00+00:00 13.06
Mean of the first 10 values: price 13.307
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 13.14
2023-09-25 10:00:00+03:00 13.20
2023-09-25 11:00:00+03:00 13.19
2023-09-25 12:00:00+03:00 13.29
2023-09-25 13:00:00+03:00 13.32
2023-09-25 14:00:00+03:00 13.27
2023-09-25 15:00:00+03:00 13.31
2023-09-25 16:00:00+03:00 13.36
2023-09-25 17:00:00+03:00 13.50
2023-09-25 18:00:00+03:00 13.49
price
2018-01-02 10:00:00+03:00 8.200000e-03
2018-01-02 11:00:00+03:00 0.000000e+00
2018-01-02 12:00:00+03:00 0.000000e+00
2018-01-02 13:00:00+03:00 7.900000e-03
2018-01-02 14:00:00+03:00 -7.900000e-03
... ...
2023-09-22 13:00:00+00:00 -2.000015e-02
2023-09-22 13:30:00+00:00 -4.000027e-02
2023-09-22 14:00:00+00:00 2.000027e-02
2023-09-22 14:30:00+00:00 1.907349e-07
2023-09-22 15:00:00+00:00 9.999809e-03
[17889 rows x 1 columns]
ADF Statistic: -21.651370306616798
p-value: 0.0
Critical Values: {'1%': -3.430716482172607, '5%': -2.861701971163161, '10%': -2.5668562130114436}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 1, 1) Log Likelihood 23798.523
Date: Sun, 24 Dec 2023 AIC -47533.046
Time: 20:57:46 BIC -47283.704
Sample: 0 HQIC -47451.026
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.3540 0.076 -4.643 0.000 -0.503 -0.205
ar.L2 0.1837 0.041 4.488 0.000 0.104 0.264
ar.L3 -0.0915 0.008 -10.947 0.000 -0.108 -0.075
ar.L4 0.0559 0.008 7.007 0.000 0.040 0.072
ar.L5 -0.0243 0.006 -3.866 0.000 -0.037 -0.012
ar.L6 0.0451 0.006 8.145 0.000 0.034 0.056
ar.L7 -0.0270 0.007 -4.107 0.000 -0.040 -0.014
ar.L8 0.0366 0.006 6.321 0.000 0.025 0.048
ar.L9 -0.0232 0.006 -3.992 0.000 -0.035 -0.012
ar.L10 0.0592 0.005 11.145 0.000 0.049 0.070
ar.L11 -0.0140 0.007 -2.067 0.039 -0.027 -0.001
ar.L12 -0.0347 0.006 -6.056 0.000 -0.046 -0.023
ar.L13 -0.0035 0.006 -0.583 0.560 -0.015 0.008
ar.L14 -0.0043 0.005 -0.792 0.429 -0.015 0.006
ar.L15 0.0200 0.005 4.181 0.000 0.011 0.029
ar.L16 -0.1278 0.005 -27.402 0.000 -0.137 -0.119
ar.L17 -0.0328 0.010 -3.139 0.002 -0.053 -0.012
ar.L18 0.1839 0.006 33.077 0.000 0.173 0.195
ar.L19 0.1695 0.014 12.389 0.000 0.143 0.196
ar.L20 -0.2146 0.016 -13.604 0.000 -0.246 -0.184
ar.L21 -0.0345 0.014 -2.428 0.015 -0.062 -0.007
ar.L22 0.0050 0.007 0.716 0.474 -0.009 0.019
ar.L23 -0.0699 0.005 -13.196 0.000 -0.080 -0.060
ar.L24 0.0248 0.008 3.177 0.001 0.009 0.040
ar.L25 -0.0182 0.006 -3.294 0.001 -0.029 -0.007
ar.L26 0.0433 0.006 7.670 0.000 0.032 0.054
ar.L27 -0.0583 0.007 -8.444 0.000 -0.072 -0.045
ar.L28 0.0102 0.007 1.403 0.161 -0.004 0.025
ar.L29 -0.0517 0.005 -10.179 0.000 -0.062 -0.042
ar.L30 0.0806 0.007 11.222 0.000 0.066 0.095
ma.L1 -0.1836 0.076 -2.414 0.016 -0.333 -0.035
sigma2 0.0041 1.74e-05 235.623 0.000 0.004 0.004
===================================================================================
Ljung-Box (L1) (Q): 0.13 Jarque-Bera (JB): 209167.98
Prob(Q): 0.72 Prob(JB): 0.00
Heteroskedasticity (H): 64.14 Skew: 0.46
Prob(H) (two-sided): 0.00 Kurtosis: 19.73
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 13.013064
17891 13.042579
17892 13.029734
17893 13.074533
17894 13.029376
17895 13.059139
17896 13.048819
17897 13.082186
17898 13.033746
17899 13.006131
Name: predicted_mean, dtype: float64
lower price upper price
17890 12.887731 13.138398
17891 12.904494 13.180665
17892 12.856359 13.203110
17893 12.889587 13.259479
17894 12.818890 13.239862
17895 12.837575 13.280704
17896 12.805520 13.292119
17897 12.828866 13.335506
17898 12.760924 13.306568
17899 12.724237 13.288025
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.28962864777279757 Weighted Mean Absolute Percentage Error (WMAPE): 1.9919527840870335
forecastplusyahoo('EKGYO', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 2.0714 EKGYO
2021-12-27 10:00:00+03:00 2.0993 EKGYO
2021-12-27 11:00:00+03:00 2.0993 EKGYO
2021-12-27 12:00:00+03:00 2.0901 EKGYO
2021-12-27 13:00:00+03:00 2.0901 EKGYO
... ... ...
2023-09-22 14:00:00+03:00 7.9100 EKGYO
2023-09-22 15:00:00+03:00 7.8900 EKGYO
2023-09-22 16:00:00+03:00 7.8900 EKGYO
2023-09-22 17:00:00+03:00 7.9000 EKGYO
2023-09-22 18:00:00+03:00 7.9100 EKGYO
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for EKGYO.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 2.0714
2021-12-27 06:30:00+00:00 2.2400
2021-12-27 07:00:00+00:00 2.0993
2021-12-27 07:30:00+00:00 2.2300
2021-12-27 08:00:00+00:00 2.0993
2021-12-27 08:30:00+00:00 2.2400
2021-12-27 09:00:00+00:00 2.0901
2021-12-27 09:30:00+00:00 2.2400
2021-12-27 10:00:00+00:00 2.0901
2021-12-27 10:30:00+00:00 2.2300
2021-12-27 11:00:00+00:00 2.0620
2021-12-27 11:30:00+00:00 2.2100
2021-12-27 12:00:00+00:00 2.0807
2021-12-27 12:30:00+00:00 2.2200
2021-12-27 13:00:00+00:00 2.0901
2021-12-27 13:30:00+00:00 2.2500
2021-12-27 14:00:00+00:00 2.0714
2021-12-27 15:00:00+00:00 2.0714
2021-12-28 06:00:00+00:00 2.1086
2021-12-28 06:30:00+00:00 2.2800
2021-12-28 07:00:00+00:00 2.1460
2021-12-28 07:30:00+00:00 2.3100
2021-12-28 08:00:00+00:00 2.1273
2021-12-28 08:30:00+00:00 2.2400
2021-12-28 09:00:00+00:00 2.0901
2021-12-28 09:30:00+00:00 2.2400
2021-12-28 10:00:00+00:00 2.0901
2021-12-28 10:30:00+00:00 2.2300
2021-12-28 11:00:00+00:00 2.0620
2021-12-28 11:30:00+00:00 2.2200
2021-12-28 12:00:00+00:00 2.0807
2021-12-28 12:30:00+00:00 2.2300
2021-12-28 13:00:00+00:00 2.0714
2021-12-28 13:30:00+00:00 2.2000
2021-12-28 14:00:00+00:00 2.0527
2021-12-28 15:00:00+00:00 2.0527
2021-12-29 06:00:00+00:00 2.0620
2021-12-29 06:30:00+00:00 2.1800
2021-12-29 07:00:00+00:00 2.0247
2021-12-29 07:30:00+00:00 2.1900
2021-12-29 08:00:00+00:00 2.0247
2021-12-29 08:30:00+00:00 2.1800
2021-12-29 09:00:00+00:00 2.0247
2021-12-29 09:30:00+00:00 2.1700
2021-12-29 10:00:00+00:00 2.0341
2021-12-29 10:30:00+00:00 2.1800
2021-12-29 11:00:00+00:00 2.0341
2021-12-29 11:30:00+00:00 2.1900
2021-12-29 12:00:00+00:00 2.0620
2021-12-29 12:30:00+00:00 2.2200
price
2023-09-21 10:00:00+00:00 7.80
2023-09-21 10:30:00+00:00 7.86
2023-09-21 11:00:00+00:00 7.92
2023-09-21 11:30:00+00:00 7.88
2023-09-21 12:00:00+00:00 7.88
2023-09-21 12:30:00+00:00 7.92
2023-09-21 13:00:00+00:00 7.91
2023-09-21 13:30:00+00:00 8.01
2023-09-21 14:00:00+00:00 8.01
2023-09-21 14:30:00+00:00 8.01
2023-09-21 15:00:00+00:00 8.03
2023-09-22 06:00:00+00:00 8.03
2023-09-22 06:30:00+00:00 8.05
2023-09-22 07:00:00+00:00 8.02
2023-09-22 07:30:00+00:00 8.05
2023-09-22 08:00:00+00:00 8.05
2023-09-22 08:30:00+00:00 8.03
2023-09-22 09:00:00+00:00 8.03
2023-09-22 09:30:00+00:00 8.00
2023-09-22 10:00:00+00:00 7.98
2023-09-22 10:30:00+00:00 7.95
2023-09-22 11:00:00+00:00 7.91
2023-09-22 11:30:00+00:00 7.88
2023-09-22 12:00:00+00:00 7.89
2023-09-22 12:30:00+00:00 7.90
2023-09-22 13:00:00+00:00 7.89
2023-09-22 13:30:00+00:00 7.89
2023-09-22 14:00:00+00:00 7.90
2023-09-22 14:30:00+00:00 7.90
2023-09-22 15:00:00+00:00 7.91
Mean of the first 10 values: price 8.061
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 7.94
2023-09-25 10:00:00+03:00 8.08
2023-09-25 11:00:00+03:00 8.08
2023-09-25 12:00:00+03:00 8.06
2023-09-25 13:00:00+03:00 8.05
2023-09-25 14:00:00+03:00 8.00
2023-09-25 15:00:00+03:00 7.99
2023-09-25 16:00:00+03:00 8.05
2023-09-25 17:00:00+03:00 8.18
2023-09-25 18:00:00+03:00 8.18
price
2018-01-02 10:00:00+03:00 2.460000e-02
2018-01-02 11:00:00+03:00 -8.100000e-03
2018-01-02 12:00:00+03:00 8.100000e-03
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 8.100000e-03
... ...
2023-09-22 13:00:00+00:00 -1.000010e-02
2023-09-22 13:30:00+00:00 -1.335144e-07
2023-09-22 14:00:00+00:00 1.000013e-02
2023-09-22 14:30:00+00:00 9.536743e-08
2023-09-22 15:00:00+00:00 9.999905e-03
[17890 rows x 1 columns]
ADF Statistic: -19.281107921662084
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17891
Model: ARIMA(30, 3, 1) Log Likelihood 28040.755
Date: Sun, 24 Dec 2023 AIC -56017.509
Time: 21:08:49 BIC -55768.169
Sample: 0 HQIC -55935.490
- 17891
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.4811 0.004 -418.252 0.000 -1.488 -1.474
ar.L2 -1.3162 0.007 -180.997 0.000 -1.330 -1.302
ar.L3 -1.3883 0.010 -133.806 0.000 -1.409 -1.368
ar.L4 -1.3749 0.013 -105.506 0.000 -1.400 -1.349
ar.L5 -1.4124 0.015 -93.288 0.000 -1.442 -1.383
ar.L6 -1.3409 0.017 -78.150 0.000 -1.374 -1.307
ar.L7 -1.3243 0.019 -69.915 0.000 -1.361 -1.287
ar.L8 -1.2535 0.020 -61.618 0.000 -1.293 -1.214
ar.L9 -1.1992 0.021 -55.918 0.000 -1.241 -1.157
ar.L10 -1.1232 0.023 -49.913 0.000 -1.167 -1.079
ar.L11 -1.0407 0.023 -44.411 0.000 -1.087 -0.995
ar.L12 -0.9706 0.024 -40.421 0.000 -1.018 -0.924
ar.L13 -0.8889 0.024 -36.641 0.000 -0.936 -0.841
ar.L14 -0.8977 0.024 -37.669 0.000 -0.944 -0.851
ar.L15 -0.8638 0.024 -36.438 0.000 -0.910 -0.817
ar.L16 -0.9658 0.024 -41.008 0.000 -1.012 -0.920
ar.L17 -1.0634 0.024 -45.193 0.000 -1.110 -1.017
ar.L18 -0.7880 0.024 -33.236 0.000 -0.834 -0.742
ar.L19 -0.5851 0.023 -25.420 0.000 -0.630 -0.540
ar.L20 -0.7642 0.022 -34.464 0.000 -0.808 -0.721
ar.L21 -0.7541 0.022 -35.004 0.000 -0.796 -0.712
ar.L22 -0.6702 0.021 -32.097 0.000 -0.711 -0.629
ar.L23 -0.5992 0.020 -29.795 0.000 -0.639 -0.560
ar.L24 -0.5620 0.019 -29.952 0.000 -0.599 -0.525
ar.L25 -0.5398 0.017 -30.876 0.000 -0.574 -0.506
ar.L26 -0.3453 0.016 -21.402 0.000 -0.377 -0.314
ar.L27 -0.1866 0.014 -13.036 0.000 -0.215 -0.159
ar.L28 -0.0397 0.012 -3.357 0.001 -0.063 -0.017
ar.L29 -0.0120 0.009 -1.296 0.195 -0.030 0.006
ar.L30 0.0628 0.005 12.558 0.000 0.053 0.073
ma.L1 -0.9545 0.002 -434.894 0.000 -0.959 -0.950
sigma2 0.0025 9.16e-06 274.313 0.000 0.002 0.003
===================================================================================
Ljung-Box (L1) (Q): 33.07 Jarque-Bera (JB): 244783.49
Prob(Q): 0.00 Prob(JB): 0.00
Heteroskedasticity (H): 32.23 Skew: 0.67
Prob(H) (two-sided): 0.00 Kurtosis: 21.07
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891 7.907070
17892 7.900599
17893 7.900568
17894 7.922457
17895 7.917572
17896 7.938448
17897 7.929075
17898 7.943442
17899 7.930265
17900 7.926018
Name: predicted_mean, dtype: float64
lower price upper price
17891 7.808801 8.005340
17892 7.787759 8.013439
17893 7.751939 8.049197
17894 7.759339 8.085574
17895 7.727748 8.107396
17896 7.734312 8.142583
17897 7.698882 8.159267
17898 7.697483 8.189400
17899 7.657439 8.203091
17900 7.634968 8.217068
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.1573689183058474 Weighted Mean Absolute Percentage Error (WMAPE): 1.7307715709708829
forecastplusyahoo('EREGL', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 25.0042 EREGL
2021-12-27 10:00:00+03:00 25.6008 EREGL
2021-12-27 11:00:00+03:00 25.4955 EREGL
2021-12-27 12:00:00+03:00 25.3902 EREGL
2021-12-27 13:00:00+03:00 25.5130 EREGL
... ... ...
2023-09-22 14:00:00+03:00 42.8400 EREGL
2023-09-22 15:00:00+03:00 43.0600 EREGL
2023-09-22 16:00:00+03:00 43.2200 EREGL
2023-09-22 17:00:00+03:00 43.1800 EREGL
2023-09-22 18:00:00+03:00 43.2800 EREGL
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for EREGL.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 25.004200
2021-12-27 06:30:00+00:00 29.120001
2021-12-27 07:00:00+00:00 25.600800
2021-12-27 07:30:00+00:00 29.020000
2021-12-27 08:00:00+00:00 25.495500
2021-12-27 08:30:00+00:00 29.020000
2021-12-27 09:00:00+00:00 25.390200
2021-12-27 09:30:00+00:00 28.900000
2021-12-27 10:00:00+00:00 25.513000
2021-12-27 10:30:00+00:00 29.020000
2021-12-27 11:00:00+00:00 25.337600
2021-12-27 11:30:00+00:00 28.600000
2021-12-27 12:00:00+00:00 25.372600
2021-12-27 12:30:00+00:00 28.879999
2021-12-27 13:00:00+00:00 25.407700
2021-12-27 13:30:00+00:00 29.080000
2021-12-27 14:00:00+00:00 25.197200
2021-12-27 15:00:00+00:00 25.197200
2021-12-28 06:00:00+00:00 25.513000
2021-12-28 06:30:00+00:00 28.879999
2021-12-28 07:00:00+00:00 25.425300
2021-12-28 07:30:00+00:00 28.940001
2021-12-28 08:00:00+00:00 25.284900
2021-12-28 08:30:00+00:00 28.799999
2021-12-28 09:00:00+00:00 25.267400
2021-12-28 09:30:00+00:00 28.920000
2021-12-28 10:00:00+00:00 25.320000
2021-12-28 10:30:00+00:00 28.760000
2021-12-28 11:00:00+00:00 25.021700
2021-12-28 11:30:00+00:00 28.719999
2021-12-28 12:00:00+00:00 25.056800
2021-12-28 12:30:00+00:00 28.540001
2021-12-28 13:00:00+00:00 25.039300
2021-12-28 13:30:00+00:00 27.920000
2021-12-28 14:00:00+00:00 24.460200
2021-12-28 15:00:00+00:00 24.407600
2021-12-29 06:00:00+00:00 24.407600
2021-12-29 06:30:00+00:00 27.940001
2021-12-29 07:00:00+00:00 24.951500
2021-12-29 07:30:00+00:00 28.700001
2021-12-29 08:00:00+00:00 25.249800
2021-12-29 08:30:00+00:00 28.719999
2021-12-29 09:00:00+00:00 25.232300
2021-12-29 09:30:00+00:00 28.860001
2021-12-29 10:00:00+00:00 25.477900
2021-12-29 10:30:00+00:00 28.820000
2021-12-29 11:00:00+00:00 25.267400
2021-12-29 11:30:00+00:00 28.980000
2021-12-29 12:00:00+00:00 25.477900
2021-12-29 12:30:00+00:00 29.219999
price
2023-09-21 10:00:00+00:00 40.420000
2023-09-21 10:30:00+00:00 40.840000
2023-09-21 11:00:00+00:00 41.260000
2023-09-21 11:30:00+00:00 41.180000
2023-09-21 12:00:00+00:00 41.100000
2023-09-21 12:30:00+00:00 41.340000
2023-09-21 13:00:00+00:00 41.280000
2023-09-21 13:30:00+00:00 41.480000
2023-09-21 14:00:00+00:00 41.720000
2023-09-21 14:30:00+00:00 41.720001
2023-09-21 15:00:00+00:00 41.740000
2023-09-22 06:00:00+00:00 41.840000
2023-09-22 06:30:00+00:00 41.900002
2023-09-22 07:00:00+00:00 42.080000
2023-09-22 07:30:00+00:00 42.080002
2023-09-22 08:00:00+00:00 42.180000
2023-09-22 08:30:00+00:00 42.299999
2023-09-22 09:00:00+00:00 42.320000
2023-09-22 09:30:00+00:00 42.139999
2023-09-22 10:00:00+00:00 42.140000
2023-09-22 10:30:00+00:00 43.000000
2023-09-22 11:00:00+00:00 42.840000
2023-09-22 11:30:00+00:00 42.840000
2023-09-22 12:00:00+00:00 43.060000
2023-09-22 12:30:00+00:00 43.180000
2023-09-22 13:00:00+00:00 43.220000
2023-09-22 13:30:00+00:00 43.119999
2023-09-22 14:00:00+00:00 43.180000
2023-09-22 14:30:00+00:00 43.180000
2023-09-22 15:00:00+00:00 43.280000
Mean of the first 10 values: price 45.784
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 43.44
2023-09-25 10:00:00+03:00 43.98
2023-09-25 11:00:00+03:00 45.16
2023-09-25 12:00:00+03:00 45.74
2023-09-25 13:00:00+03:00 46.80
2023-09-25 14:00:00+03:00 46.68
2023-09-25 15:00:00+03:00 46.60
2023-09-25 16:00:00+03:00 46.74
2023-09-25 17:00:00+03:00 46.38
2023-09-25 18:00:00+03:00 46.32
price
2018-01-02 10:00:00+03:00 1.780000e-02
2018-01-02 11:00:00+03:00 -2.370000e-02
2018-01-02 12:00:00+03:00 0.000000e+00
2018-01-02 13:00:00+03:00 1.180000e-02
2018-01-02 14:00:00+03:00 2.370000e-02
... ...
2023-09-22 13:00:00+00:00 3.999969e-02
2023-09-22 13:30:00+00:00 -1.000011e-01
2023-09-22 14:00:00+00:00 6.000107e-02
2023-09-22 14:30:00+00:00 3.051758e-07
2023-09-22 15:00:00+00:00 9.999969e-02
[17889 rows x 1 columns]
ADF Statistic: -21.727947513805333
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 3, 1) Log Likelihood -1053.501
Date: Sun, 24 Dec 2023 AIC 2171.002
Time: 21:28:11 BIC 2420.340
Sample: 0 HQIC 2253.021
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.3765 0.003 -457.849 0.000 -1.382 -1.371
ar.L2 -1.2377 0.006 -190.941 0.000 -1.250 -1.225
ar.L3 -1.3343 0.010 -140.370 0.000 -1.353 -1.316
ar.L4 -1.2429 0.012 -99.967 0.000 -1.267 -1.219
ar.L5 -1.3181 0.015 -89.838 0.000 -1.347 -1.289
ar.L6 -1.2403 0.017 -74.212 0.000 -1.273 -1.208
ar.L7 -1.2283 0.018 -66.986 0.000 -1.264 -1.192
ar.L8 -1.1791 0.020 -59.949 0.000 -1.218 -1.141
ar.L9 -1.1382 0.021 -54.670 0.000 -1.179 -1.097
ar.L10 -1.0907 0.022 -50.436 0.000 -1.133 -1.048
ar.L11 -1.0390 0.022 -46.606 0.000 -1.083 -0.995
ar.L12 -1.0399 0.023 -45.935 0.000 -1.084 -0.996
ar.L13 -0.9634 0.023 -41.410 0.000 -1.009 -0.918
ar.L14 -0.9213 0.024 -39.074 0.000 -0.967 -0.875
ar.L15 -0.8872 0.024 -37.622 0.000 -0.933 -0.841
ar.L16 -0.9493 0.024 -40.130 0.000 -0.996 -0.903
ar.L17 -1.0714 0.024 -45.159 0.000 -1.118 -1.025
ar.L18 -0.4291 0.024 -17.970 0.000 -0.476 -0.382
ar.L19 -0.3066 0.023 -13.381 0.000 -0.351 -0.262
ar.L20 -0.5088 0.022 -23.356 0.000 -0.552 -0.466
ar.L21 -0.4655 0.021 -22.257 0.000 -0.507 -0.425
ar.L22 -0.5128 0.020 -25.907 0.000 -0.552 -0.474
ar.L23 -0.4007 0.019 -21.209 0.000 -0.438 -0.364
ar.L24 -0.3859 0.018 -22.018 0.000 -0.420 -0.352
ar.L25 -0.3064 0.016 -19.106 0.000 -0.338 -0.275
ar.L26 -0.2431 0.015 -16.478 0.000 -0.272 -0.214
ar.L27 -0.1774 0.013 -13.334 0.000 -0.204 -0.151
ar.L28 -0.1202 0.011 -11.122 0.000 -0.141 -0.099
ar.L29 -0.0733 0.009 -8.431 0.000 -0.090 -0.056
ar.L30 0.0044 0.005 0.928 0.354 -0.005 0.014
ma.L1 -0.9499 0.002 -433.585 0.000 -0.954 -0.946
sigma2 0.0657 0.000 397.286 0.000 0.065 0.066
===================================================================================
Ljung-Box (L1) (Q): 0.11 Jarque-Bera (JB): 2064099.53
Prob(Q): 0.74 Prob(JB): 0.00
Heteroskedasticity (H): 23.47 Skew: 0.37
Prob(H) (two-sided): 0.00 Kurtosis: 55.62
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 43.422714
17891 43.675701
17892 43.681565
17893 43.888397
17894 44.031904
17895 44.185842
17896 44.049971
17897 44.147177
17898 44.822329
17899 44.707659
Name: predicted_mean, dtype: float64
lower price upper price
17890 42.920310 43.925118
17891 43.069945 44.281457
17892 42.899235 44.463894
17893 43.014276 44.762517
17894 43.005360 45.058449
17895 43.074791 45.296893
17896 42.792281 45.307660
17897 42.795792 45.498561
17898 43.324663 46.319996
17899 43.105414 46.309904
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 1.9452928352152923 Weighted Mean Absolute Percentage Error (WMAPE): 4.124983996856018
forecastplusyahoo('FROTO', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 216.8891 FROTO
2021-12-27 10:00:00+03:00 225.8110 FROTO
2021-12-27 11:00:00+03:00 223.8482 FROTO
2021-12-27 12:00:00+03:00 221.4393 FROTO
2021-12-27 13:00:00+03:00 221.6177 FROTO
... ... ...
2023-09-22 14:00:00+03:00 810.1695 FROTO
2023-09-22 15:00:00+03:00 808.0466 FROTO
2023-09-22 16:00:00+03:00 809.2045 FROTO
2023-09-22 17:00:00+03:00 812.4853 FROTO
2023-09-22 18:00:00+03:00 809.9765 FROTO
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for FROTO.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 216.889100
2021-12-27 06:30:00+00:00 250.199997
2021-12-27 07:00:00+00:00 225.811000
2021-12-27 07:30:00+00:00 251.100006
2021-12-27 08:00:00+00:00 223.848200
2021-12-27 08:30:00+00:00 249.699997
2021-12-27 09:00:00+00:00 221.439300
2021-12-27 09:30:00+00:00 247.300003
2021-12-27 10:00:00+00:00 221.617700
2021-12-27 10:30:00+00:00 247.500000
2021-12-27 11:00:00+00:00 217.959700
2021-12-27 11:30:00+00:00 242.000000
2021-12-27 12:00:00+00:00 218.584400
2021-12-27 12:30:00+00:00 244.000000
2021-12-27 13:00:00+00:00 218.673500
2021-12-27 13:30:00+00:00 246.600006
2021-12-27 14:00:00+00:00 218.495200
2021-12-27 15:00:00+00:00 217.781400
2021-12-28 06:00:00+00:00 219.922500
2021-12-28 06:30:00+00:00 245.500000
2021-12-28 07:00:00+00:00 219.565700
2021-12-28 07:30:00+00:00 243.899994
2021-12-28 08:00:00+00:00 216.889100
2021-12-28 08:30:00+00:00 243.699997
2021-12-28 09:00:00+00:00 216.532300
2021-12-28 09:30:00+00:00 242.699997
2021-12-28 10:00:00+00:00 217.513700
2021-12-28 10:30:00+00:00 241.699997
2021-12-28 11:00:00+00:00 214.301800
2021-12-28 11:30:00+00:00 241.899994
2021-12-28 12:00:00+00:00 216.621500
2021-12-28 12:30:00+00:00 241.899994
2021-12-28 13:00:00+00:00 214.480300
2021-12-28 13:30:00+00:00 237.399994
2021-12-28 14:00:00+00:00 208.948700
2021-12-28 15:00:00+00:00 212.428200
2021-12-29 06:00:00+00:00 215.818600
2021-12-29 06:30:00+00:00 239.300003
2021-12-29 07:00:00+00:00 213.588100
2021-12-29 07:30:00+00:00 241.500000
2021-12-29 08:00:00+00:00 216.175400
2021-12-29 08:30:00+00:00 243.199997
2021-12-29 09:00:00+00:00 217.602900
2021-12-29 09:30:00+00:00 242.500000
2021-12-29 10:00:00+00:00 216.443100
2021-12-29 10:30:00+00:00 240.800003
2021-12-29 11:00:00+00:00 216.264600
2021-12-29 11:30:00+00:00 244.000000
2021-12-29 12:00:00+00:00 217.959700
2021-12-29 12:30:00+00:00 244.800003
price
2023-09-21 10:00:00+00:00 795.598800
2023-09-21 10:30:00+00:00 829.799988
2023-09-21 11:00:00+00:00 807.564100
2023-09-21 11:30:00+00:00 835.500000
2023-09-21 12:00:00+00:00 803.897300
2023-09-21 12:30:00+00:00 840.299988
2023-09-21 13:00:00+00:00 806.985100
2023-09-21 13:30:00+00:00 844.599976
2023-09-21 14:00:00+00:00 819.529400
2023-09-21 14:30:00+00:00 849.299988
2023-09-21 15:00:00+00:00 819.722400
2023-09-22 06:00:00+00:00 819.722400
2023-09-22 06:30:00+00:00 848.700012
2023-09-22 07:00:00+00:00 815.476700
2023-09-22 07:30:00+00:00 847.299988
2023-09-22 08:00:00+00:00 816.731100
2023-09-22 08:30:00+00:00 850.200012
2023-09-22 09:00:00+00:00 818.661000
2023-09-22 09:30:00+00:00 846.099976
2023-09-22 10:00:00+00:00 815.090700
2023-09-22 10:30:00+00:00 841.799988
2023-09-22 11:00:00+00:00 810.169500
2023-09-22 11:30:00+00:00 835.799988
2023-09-22 12:00:00+00:00 808.046600
2023-09-22 12:30:00+00:00 839.500000
2023-09-22 13:00:00+00:00 809.204500
2023-09-22 13:30:00+00:00 836.799988
2023-09-22 14:00:00+00:00 812.485300
2023-09-22 14:30:00+00:00 842.000000
2023-09-22 15:00:00+00:00 809.976500
Mean of the first 10 values: price 812.85202
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 813.8363
2023-09-25 10:00:00+03:00 804.9587
2023-09-25 11:00:00+03:00 806.6957
2023-09-25 12:00:00+03:00 812.0994
2023-09-25 13:00:00+03:00 811.1344
2023-09-25 14:00:00+03:00 805.7307
2023-09-25 15:00:00+03:00 809.3010
2023-09-25 16:00:00+03:00 823.9682
2023-09-25 17:00:00+03:00 820.3979
2023-09-25 18:00:00+03:00 820.3979
price
2018-01-02 10:00:00+03:00 0.209400
2018-01-02 11:00:00+03:00 0.314200
2018-01-02 12:00:00+03:00 0.174400
2018-01-02 13:00:00+03:00 -0.139500
2018-01-02 14:00:00+03:00 0.279100
... ...
2023-09-22 13:00:00+00:00 -30.295500
2023-09-22 13:30:00+00:00 27.595488
2023-09-22 14:00:00+00:00 -24.314688
2023-09-22 14:30:00+00:00 29.514700
2023-09-22 15:00:00+00:00 -32.023500
[17888 rows x 1 columns]
ADF Statistic: -21.61039641010989
p-value: 0.0
Critical Values: {'1%': -3.4307165437992815, '5%': -2.861701998398123, '10%': -2.5668562275081928}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17889
Model: ARIMA(30, 1, 1) Log Likelihood -53978.006
Date: Sun, 24 Dec 2023 AIC 108020.012
Time: 21:13:57 BIC 108269.353
Sample: 0 HQIC 108102.032
- 17889
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.7040 0.150 -4.695 0.000 -0.998 -0.410
ar.L2 -0.0208 0.139 -0.150 0.881 -0.293 0.252
ar.L3 -0.0442 0.034 -1.302 0.193 -0.111 0.022
ar.L4 0.0057 0.015 0.384 0.701 -0.024 0.035
ar.L5 -0.0678 0.006 -11.011 0.000 -0.080 -0.056
ar.L6 0.0079 0.012 0.644 0.520 -0.016 0.032
ar.L7 -0.0269 0.007 -4.008 0.000 -0.040 -0.014
ar.L8 0.0352 0.008 4.378 0.000 0.019 0.051
ar.L9 -0.0361 0.008 -4.615 0.000 -0.051 -0.021
ar.L10 0.0394 0.009 4.509 0.000 0.022 0.057
ar.L11 -0.0058 0.008 -0.733 0.464 -0.021 0.010
ar.L12 -0.0007 0.007 -0.103 0.918 -0.014 0.013
ar.L13 -0.0109 0.007 -1.662 0.097 -0.024 0.002
ar.L14 -0.0242 0.006 -3.896 0.000 -0.036 -0.012
ar.L15 -0.0283 0.007 -4.132 0.000 -0.042 -0.015
ar.L16 -0.1744 0.007 -25.157 0.000 -0.188 -0.161
ar.L17 -0.0344 0.028 -1.245 0.213 -0.089 0.020
ar.L18 0.2606 0.012 22.126 0.000 0.238 0.284
ar.L19 0.4876 0.037 13.240 0.000 0.415 0.560
ar.L20 -0.0635 0.082 -0.780 0.436 -0.223 0.096
ar.L21 -0.1861 0.010 -18.062 0.000 -0.206 -0.166
ar.L22 -0.0377 0.027 -1.414 0.157 -0.090 0.015
ar.L23 -0.0699 0.013 -5.396 0.000 -0.095 -0.044
ar.L24 0.0003 0.015 0.021 0.984 -0.028 0.029
ar.L25 -0.0571 0.007 -7.723 0.000 -0.072 -0.043
ar.L26 0.0200 0.012 1.702 0.089 -0.003 0.043
ar.L27 -0.0194 0.007 -2.680 0.007 -0.034 -0.005
ar.L28 0.0317 0.007 4.308 0.000 0.017 0.046
ar.L29 -0.0426 0.008 -5.199 0.000 -0.059 -0.027
ar.L30 0.0522 0.011 4.854 0.000 0.031 0.073
ma.L1 -0.2195 0.150 -1.464 0.143 -0.513 0.074
sigma2 24.1995 0.119 203.778 0.000 23.967 24.432
===================================================================================
Ljung-Box (L1) (Q): 0.10 Jarque-Bera (JB): 100499.86
Prob(Q): 0.75 Prob(JB): 0.00
Heteroskedasticity (H): 181.41 Skew: -0.28
Prob(H) (two-sided): 0.00 Kurtosis: 14.60
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17889 830.993420
17890 831.416583
17891 816.415864
17892 836.667463
17893 815.888341
17894 839.912712
17895 814.744336
17896 837.258024
17897 815.209181
17898 835.084600
Name: predicted_mean, dtype: float64
lower price upper price
17889 821.351778 840.635062
17890 821.746796 841.086369
17891 804.591520 828.240208
17892 824.623002 848.711924
17893 802.523481 829.253201
17894 826.325402 853.500022
17895 800.031282 829.457390
17896 822.326924 852.189124
17897 799.205572 831.212789
17898 818.916276 851.252925
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 18.254694830973946 Weighted Mean Absolute Percentage Error (WMAPE): 1.9625575975106204
forecastplusyahoo('GUBRF', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 77.30 GUBRF
2021-12-27 10:00:00+03:00 81.00 GUBRF
2021-12-27 11:00:00+03:00 81.15 GUBRF
2021-12-27 12:00:00+03:00 82.35 GUBRF
2021-12-27 13:00:00+03:00 83.00 GUBRF
... ... ...
2023-09-22 14:00:00+03:00 337.20 GUBRF
2023-09-22 15:00:00+03:00 337.90 GUBRF
2023-09-22 16:00:00+03:00 337.40 GUBRF
2023-09-22 17:00:00+03:00 339.70 GUBRF
2023-09-22 18:00:00+03:00 339.50 GUBRF
[4323 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for GUBRF.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 77.300000
2021-12-27 06:30:00+00:00 79.150002
2021-12-27 07:00:00+00:00 81.000000
2021-12-27 07:30:00+00:00 80.949997
2021-12-27 08:00:00+00:00 81.150000
2021-12-27 08:30:00+00:00 82.150002
2021-12-27 09:00:00+00:00 82.350000
2021-12-27 09:30:00+00:00 82.300003
2021-12-27 10:00:00+00:00 83.000000
2021-12-27 10:30:00+00:00 82.900002
2021-12-27 11:00:00+00:00 82.500000
2021-12-27 11:30:00+00:00 82.050003
2021-12-27 12:00:00+00:00 83.150000
2021-12-27 12:30:00+00:00 82.650002
2021-12-27 13:00:00+00:00 83.100000
2021-12-27 13:30:00+00:00 83.050003
2021-12-27 14:00:00+00:00 83.150000
2021-12-27 15:00:00+00:00 83.500000
2021-12-28 06:00:00+00:00 85.600000
2021-12-28 06:30:00+00:00 84.349998
2021-12-28 07:00:00+00:00 84.250000
2021-12-28 07:30:00+00:00 83.800003
2021-12-28 08:00:00+00:00 82.850000
2021-12-28 08:30:00+00:00 82.900002
2021-12-28 09:00:00+00:00 82.900000
2021-12-28 09:30:00+00:00 82.949997
2021-12-28 10:00:00+00:00 83.500000
2021-12-28 10:30:00+00:00 84.250000
2021-12-28 11:00:00+00:00 83.950000
2021-12-28 11:30:00+00:00 84.199997
2021-12-28 12:00:00+00:00 83.600000
2021-12-28 12:30:00+00:00 83.250000
2021-12-28 13:00:00+00:00 83.000000
2021-12-28 13:30:00+00:00 81.349998
2021-12-28 14:00:00+00:00 81.850000
2021-12-28 15:00:00+00:00 81.850000
2021-12-29 06:00:00+00:00 81.900000
2021-12-29 06:30:00+00:00 81.300003
2021-12-29 07:00:00+00:00 81.350000
2021-12-29 07:30:00+00:00 82.400002
2021-12-29 08:00:00+00:00 82.000000
2021-12-29 08:30:00+00:00 81.750000
2021-12-29 09:00:00+00:00 81.800000
2021-12-29 09:30:00+00:00 82.000000
2021-12-29 10:00:00+00:00 82.000000
2021-12-29 10:30:00+00:00 81.900002
2021-12-29 11:00:00+00:00 81.650000
2021-12-29 11:30:00+00:00 82.000000
2021-12-29 12:00:00+00:00 81.800000
2021-12-29 12:30:00+00:00 82.000000
price
2023-09-21 10:00:00+00:00 328.800000
2023-09-21 10:30:00+00:00 330.899994
2023-09-21 11:00:00+00:00 332.800000
2023-09-21 11:30:00+00:00 333.200012
2023-09-21 12:00:00+00:00 335.400000
2023-09-21 12:30:00+00:00 336.899994
2023-09-21 13:00:00+00:00 337.700000
2023-09-21 13:30:00+00:00 339.399994
2023-09-21 14:00:00+00:00 340.600000
2023-09-21 14:30:00+00:00 340.600006
2023-09-21 15:00:00+00:00 340.500000
2023-09-22 06:00:00+00:00 341.500000
2023-09-22 06:30:00+00:00 340.500000
2023-09-22 07:00:00+00:00 339.600000
2023-09-22 07:30:00+00:00 340.700012
2023-09-22 08:00:00+00:00 340.600000
2023-09-22 08:30:00+00:00 339.899994
2023-09-22 09:00:00+00:00 338.700000
2023-09-22 09:30:00+00:00 337.100006
2023-09-22 10:00:00+00:00 339.700000
2023-09-22 10:30:00+00:00 336.799988
2023-09-22 11:00:00+00:00 337.200000
2023-09-22 11:30:00+00:00 338.000000
2023-09-22 12:00:00+00:00 337.900000
2023-09-22 12:30:00+00:00 339.899994
2023-09-22 13:00:00+00:00 337.400000
2023-09-22 13:30:00+00:00 336.600006
2023-09-22 14:00:00+00:00 339.700000
2023-09-22 14:30:00+00:00 339.700012
2023-09-22 15:00:00+00:00 339.500000
Mean of the first 10 values: price 344.27
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 342.0
2023-09-25 10:00:00+03:00 342.7
2023-09-25 11:00:00+03:00 342.5
2023-09-25 12:00:00+03:00 342.4
2023-09-25 13:00:00+03:00 342.3
2023-09-25 14:00:00+03:00 340.7
2023-09-25 15:00:00+03:00 345.7
2023-09-25 16:00:00+03:00 347.9
2023-09-25 17:00:00+03:00 348.0
2023-09-25 18:00:00+03:00 348.5
price
2018-01-02 10:00:00+03:00 -0.010000
2018-01-02 11:00:00+03:00 0.030000
2018-01-02 12:00:00+03:00 0.030000
2018-01-02 13:00:00+03:00 0.010000
2018-01-02 14:00:00+03:00 0.030000
... ...
2023-09-22 13:00:00+00:00 -2.499994
2023-09-22 13:30:00+00:00 -0.799994
2023-09-22 14:00:00+00:00 3.099994
2023-09-22 14:30:00+00:00 0.000012
2023-09-22 15:00:00+00:00 -0.200012
[17879 rows x 1 columns]
ADF Statistic: -18.346514139057238
p-value: 2.2407933134886963e-30
Critical Values: {'1%': -3.430716605446685, '5%': -2.861702025642245, '10%': -2.566856242009818}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17880
Model: ARIMA(30, 3, 1) Log Likelihood -28209.135
Date: Sun, 24 Dec 2023 AIC 56482.270
Time: 21:35:02 BIC 56731.590
Sample: 0 HQIC 56564.285
- 17880
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.9813 0.003 -316.841 0.000 -0.987 -0.975
ar.L2 -0.9403 0.004 -213.397 0.000 -0.949 -0.932
ar.L3 -0.9096 0.006 -155.419 0.000 -0.921 -0.898
ar.L4 -0.8915 0.007 -126.073 0.000 -0.905 -0.878
ar.L5 -0.8836 0.007 -119.057 0.000 -0.898 -0.869
ar.L6 -0.8683 0.008 -103.749 0.000 -0.885 -0.852
ar.L7 -0.8286 0.009 -89.396 0.000 -0.847 -0.810
ar.L8 -0.7895 0.010 -78.777 0.000 -0.809 -0.770
ar.L9 -0.7583 0.010 -73.325 0.000 -0.779 -0.738
ar.L10 -0.7503 0.011 -70.524 0.000 -0.771 -0.729
ar.L11 -0.7133 0.011 -63.259 0.000 -0.735 -0.691
ar.L12 -0.6719 0.011 -60.220 0.000 -0.694 -0.650
ar.L13 -0.6437 0.011 -57.305 0.000 -0.666 -0.622
ar.L14 -0.6028 0.011 -55.124 0.000 -0.624 -0.581
ar.L15 -0.5796 0.011 -53.992 0.000 -0.601 -0.559
ar.L16 -0.5621 0.010 -53.683 0.000 -0.583 -0.542
ar.L17 -0.5200 0.010 -51.591 0.000 -0.540 -0.500
ar.L18 -0.4333 0.011 -41.064 0.000 -0.454 -0.413
ar.L19 -0.3758 0.010 -36.270 0.000 -0.396 -0.355
ar.L20 -0.3707 0.010 -37.189 0.000 -0.390 -0.351
ar.L21 -0.3597 0.009 -38.104 0.000 -0.378 -0.341
ar.L22 -0.3250 0.009 -35.540 0.000 -0.343 -0.307
ar.L23 -0.2597 0.009 -29.713 0.000 -0.277 -0.243
ar.L24 -0.2187 0.008 -26.737 0.000 -0.235 -0.203
ar.L25 -0.1853 0.008 -24.328 0.000 -0.200 -0.170
ar.L26 -0.1359 0.007 -18.168 0.000 -0.151 -0.121
ar.L27 -0.1201 0.007 -17.081 0.000 -0.134 -0.106
ar.L28 -0.0886 0.006 -14.603 0.000 -0.100 -0.077
ar.L29 -0.0960 0.005 -18.251 0.000 -0.106 -0.086
ar.L30 -0.0484 0.004 -13.251 0.000 -0.056 -0.041
ma.L1 -1.0000 0.008 -125.650 0.000 -1.016 -0.984
sigma2 1.3730 0.010 142.562 0.000 1.354 1.392
===================================================================================
Ljung-Box (L1) (Q): 0.03 Jarque-Bera (JB): 2054817.20
Prob(Q): 0.86 Prob(JB): 0.00
Heteroskedasticity (H): 466.66 Skew: -0.12
Prob(H) (two-sided): 0.00 Kurtosis: 55.52
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17880 339.877964
17881 340.166978
17882 340.508472
17883 340.843389
17884 340.991366
17885 341.172664
17886 341.267921
17887 341.425627
17888 341.553862
17889 341.530783
Name: predicted_mean, dtype: float64
lower price upper price
17880 337.581292 342.174635
17881 336.888317 343.445640
17882 336.424535 344.592408
17883 336.050235 345.636544
17884 335.558650 346.424082
17885 335.157064 347.188264
17886 334.704431 347.831411
17887 334.319789 348.531466
17888 333.908299 349.199425
17889 333.350842 349.710723
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 4.134342823553692 Weighted Mean Absolute Percentage Error (WMAPE): 0.9916082336386941
forecastplusyahoo('GARAN', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 10.6645 GARAN
2021-12-27 10:00:00+03:00 10.7373 GARAN
2021-12-27 11:00:00+03:00 10.7282 GARAN
2021-12-27 12:00:00+03:00 10.7282 GARAN
2021-12-27 13:00:00+03:00 10.7191 GARAN
... ... ...
2023-09-22 14:00:00+03:00 51.3000 GARAN
2023-09-22 15:00:00+03:00 51.2500 GARAN
2023-09-22 16:00:00+03:00 51.4500 GARAN
2023-09-22 17:00:00+03:00 50.8500 GARAN
2023-09-22 18:00:00+03:00 50.8000 GARAN
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for GARAN.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 10.6645
2021-12-27 06:30:00+00:00 11.7300
2021-12-27 07:00:00+00:00 10.7373
2021-12-27 07:30:00+00:00 11.7700
2021-12-27 08:00:00+00:00 10.7282
2021-12-27 08:30:00+00:00 11.7900
2021-12-27 09:00:00+00:00 10.7282
2021-12-27 09:30:00+00:00 11.7600
2021-12-27 10:00:00+00:00 10.7191
2021-12-27 10:30:00+00:00 11.7700
2021-12-27 11:00:00+00:00 10.6736
2021-12-27 11:30:00+00:00 11.7200
2021-12-27 12:00:00+00:00 10.6645
2021-12-27 12:30:00+00:00 11.7100
2021-12-27 13:00:00+00:00 10.6645
2021-12-27 13:30:00+00:00 11.7500
2021-12-27 14:00:00+00:00 10.6372
2021-12-27 15:00:00+00:00 10.6190
2021-12-28 06:00:00+00:00 10.6009
2021-12-28 06:30:00+00:00 11.5900
2021-12-28 07:00:00+00:00 10.5371
2021-12-28 07:30:00+00:00 11.5800
2021-12-28 08:00:00+00:00 10.5007
2021-12-28 08:30:00+00:00 11.4900
2021-12-28 09:00:00+00:00 10.4553
2021-12-28 09:30:00+00:00 11.4700
2021-12-28 10:00:00+00:00 10.4462
2021-12-28 10:30:00+00:00 11.4800
2021-12-28 11:00:00+00:00 10.4189
2021-12-28 11:30:00+00:00 11.4800
2021-12-28 12:00:00+00:00 10.4553
2021-12-28 12:30:00+00:00 11.4800
2021-12-28 13:00:00+00:00 10.4280
2021-12-28 13:30:00+00:00 11.2300
2021-12-28 14:00:00+00:00 10.2278
2021-12-28 15:00:00+00:00 10.2278
2021-12-29 06:00:00+00:00 10.1913
2021-12-29 06:30:00+00:00 11.2300
2021-12-29 07:00:00+00:00 10.2642
2021-12-29 07:30:00+00:00 11.4000
2021-12-29 08:00:00+00:00 10.4371
2021-12-29 08:30:00+00:00 11.4700
2021-12-29 09:00:00+00:00 10.4280
2021-12-29 09:30:00+00:00 11.4500
2021-12-29 10:00:00+00:00 10.4553
2021-12-29 10:30:00+00:00 11.4500
2021-12-29 11:00:00+00:00 10.4189
2021-12-29 11:30:00+00:00 11.4600
2021-12-29 12:00:00+00:00 10.4098
2021-12-29 12:30:00+00:00 11.4700
price
2023-09-21 10:00:00+00:00 52.900000
2023-09-21 10:30:00+00:00 50.849998
2023-09-21 11:00:00+00:00 51.200000
2023-09-21 11:30:00+00:00 50.950001
2023-09-21 12:00:00+00:00 51.150000
2023-09-21 12:30:00+00:00 51.750000
2023-09-21 13:00:00+00:00 51.900000
2023-09-21 13:30:00+00:00 51.900002
2023-09-21 14:00:00+00:00 51.850000
2023-09-21 14:30:00+00:00 51.849998
2023-09-21 15:00:00+00:00 52.000000
2023-09-22 06:00:00+00:00 52.000000
2023-09-22 06:30:00+00:00 51.750000
2023-09-22 07:00:00+00:00 51.600000
2023-09-22 07:30:00+00:00 51.900002
2023-09-22 08:00:00+00:00 51.750000
2023-09-22 08:30:00+00:00 51.650002
2023-09-22 09:00:00+00:00 51.750000
2023-09-22 09:30:00+00:00 51.849998
2023-09-22 10:00:00+00:00 52.050000
2023-09-22 10:30:00+00:00 51.700001
2023-09-22 11:00:00+00:00 51.300000
2023-09-22 11:30:00+00:00 51.299999
2023-09-22 12:00:00+00:00 51.250000
2023-09-22 12:30:00+00:00 51.799999
2023-09-22 13:00:00+00:00 51.450000
2023-09-22 13:30:00+00:00 51.299999
2023-09-22 14:00:00+00:00 50.850000
2023-09-22 14:30:00+00:00 50.849998
2023-09-22 15:00:00+00:00 50.800000
Mean of the first 10 values: price 51.44
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 50.95
2023-09-25 10:00:00+03:00 51.05
2023-09-25 11:00:00+03:00 51.20
2023-09-25 12:00:00+03:00 51.35
2023-09-25 13:00:00+03:00 51.85
2023-09-25 14:00:00+03:00 51.40
2023-09-25 15:00:00+03:00 51.40
2023-09-25 16:00:00+03:00 52.05
2023-09-25 17:00:00+03:00 51.60
2023-09-25 18:00:00+03:00 51.55
price
2018-01-02 10:00:00+03:00 0.111000
2018-01-02 11:00:00+03:00 0.025700
2018-01-02 12:00:00+03:00 -0.017200
2018-01-02 13:00:00+03:00 0.008600
2018-01-02 14:00:00+03:00 0.008600
... ...
2023-09-22 13:00:00+00:00 -0.349999
2023-09-22 13:30:00+00:00 -0.150001
2023-09-22 14:00:00+00:00 -0.449999
2023-09-22 14:30:00+00:00 -0.000002
2023-09-22 15:00:00+00:00 -0.049998
[17890 rows x 1 columns]
ADF Statistic: -19.401740717527787
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17891
Model: ARIMA(30, 1, 1) Log Likelihood -3854.045
Date: Sun, 24 Dec 2023 AIC 7772.090
Time: 21:19:02 BIC 8021.434
Sample: 0 HQIC 7854.110
- 17891
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.8538 0.096 -8.924 0.000 -1.041 -0.666
ar.L2 -0.1218 0.071 -1.706 0.088 -0.262 0.018
ar.L3 -0.0691 0.007 -10.623 0.000 -0.082 -0.056
ar.L4 -0.0008 0.009 -0.093 0.926 -0.018 0.016
ar.L5 -0.0241 0.006 -3.862 0.000 -0.036 -0.012
ar.L6 0.0356 0.007 5.251 0.000 0.022 0.049
ar.L7 -0.0196 0.008 -2.553 0.011 -0.035 -0.005
ar.L8 0.0143 0.008 1.822 0.068 -0.001 0.030
ar.L9 -0.0220 0.008 -2.904 0.004 -0.037 -0.007
ar.L10 0.0278 0.008 3.518 0.000 0.012 0.043
ar.L11 -0.0072 0.007 -0.991 0.322 -0.021 0.007
ar.L12 -0.0195 0.007 -2.997 0.003 -0.032 -0.007
ar.L13 -0.0149 0.006 -2.352 0.019 -0.027 -0.002
ar.L14 -0.0283 0.006 -4.436 0.000 -0.041 -0.016
ar.L15 -0.0006 0.006 -0.097 0.923 -0.013 0.012
ar.L16 -0.1524 0.005 -30.835 0.000 -0.162 -0.143
ar.L17 -0.0943 0.015 -6.195 0.000 -0.124 -0.064
ar.L18 0.2712 0.008 32.134 0.000 0.255 0.288
ar.L19 0.4504 0.027 16.777 0.000 0.398 0.503
ar.L20 -0.0252 0.040 -0.625 0.532 -0.104 0.054
ar.L21 -0.1218 0.008 -14.708 0.000 -0.138 -0.106
ar.L22 -0.0250 0.012 -2.015 0.044 -0.049 -0.001
ar.L23 -0.0732 0.006 -11.893 0.000 -0.085 -0.061
ar.L24 -0.0224 0.009 -2.359 0.018 -0.041 -0.004
ar.L25 -0.0444 0.007 -6.256 0.000 -0.058 -0.030
ar.L26 0.0134 0.008 1.665 0.096 -0.002 0.029
ar.L27 -0.0295 0.007 -4.017 0.000 -0.044 -0.015
ar.L28 0.0108 0.009 1.256 0.209 -0.006 0.028
ar.L29 -0.0237 0.007 -3.455 0.001 -0.037 -0.010
ar.L30 0.0558 0.008 6.873 0.000 0.040 0.072
ma.L1 0.1107 0.096 1.156 0.248 -0.077 0.298
sigma2 0.0901 0.000 248.443 0.000 0.089 0.091
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 259823.74
Prob(Q): 0.94 Prob(JB): 0.00
Heteroskedasticity (H): 34.11 Skew: -0.33
Prob(H) (two-sided): 0.00 Kurtosis: 21.66
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891 50.837913
17892 50.471711
17893 50.867086
17894 50.644726
17895 50.750837
17896 50.609562
17897 50.888290
17898 50.820875
17899 50.905305
17900 50.538152
Name: predicted_mean, dtype: float64
lower price upper price
17891 50.249661 51.426165
17892 49.864355 51.079067
17893 50.109596 51.624576
17894 49.859239 51.430212
17895 49.864852 51.636822
17896 49.693565 51.525559
17897 49.885751 51.890830
17898 49.791933 51.849817
17899 49.799110 52.011500
17900 49.408720 51.667583
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.7789229266620964 Weighted Mean Absolute Percentage Error (WMAPE): 1.3735503100311612
forecastplusyahoo('KRDMD', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 8.8236 KRDMD
2021-12-27 10:00:00+03:00 9.1310 KRDMD
2021-12-27 11:00:00+03:00 9.0565 KRDMD
2021-12-27 12:00:00+03:00 9.0472 KRDMD
2021-12-27 13:00:00+03:00 9.0006 KRDMD
... ... ...
2023-09-22 14:00:00+03:00 27.3200 KRDMD
2023-09-22 15:00:00+03:00 27.4000 KRDMD
2023-09-22 16:00:00+03:00 27.5600 KRDMD
2023-09-22 17:00:00+03:00 27.6200 KRDMD
2023-09-22 18:00:00+03:00 27.5200 KRDMD
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for KRDMD.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 8.8236
2021-12-27 06:30:00+00:00 9.6900
2021-12-27 07:00:00+00:00 9.1310
2021-12-27 07:30:00+00:00 9.7100
2021-12-27 08:00:00+00:00 9.0565
2021-12-27 08:30:00+00:00 9.7100
2021-12-27 09:00:00+00:00 9.0472
2021-12-27 09:30:00+00:00 9.6100
2021-12-27 10:00:00+00:00 9.0006
2021-12-27 10:30:00+00:00 9.6800
2021-12-27 11:00:00+00:00 8.9075
2021-12-27 11:30:00+00:00 9.4500
2021-12-27 12:00:00+00:00 8.8329
2021-12-27 12:30:00+00:00 9.4700
2021-12-27 13:00:00+00:00 8.8329
2021-12-27 13:30:00+00:00 9.5200
2021-12-27 14:00:00+00:00 8.7489
2021-12-27 15:00:00+00:00 8.7397
2021-12-28 06:00:00+00:00 8.8702
2021-12-28 06:30:00+00:00 9.4700
2021-12-28 07:00:00+00:00 8.8049
2021-12-28 07:30:00+00:00 9.4600
2021-12-28 08:00:00+00:00 8.7770
2021-12-28 08:30:00+00:00 9.3700
2021-12-28 09:00:00+00:00 8.7584
2021-12-28 09:30:00+00:00 9.3900
2021-12-28 10:00:00+00:00 8.7304
2021-12-28 10:30:00+00:00 9.3500
2021-12-28 11:00:00+00:00 8.5813
2021-12-28 11:30:00+00:00 9.2700
2021-12-28 12:00:00+00:00 8.6373
2021-12-28 12:30:00+00:00 9.2300
2021-12-28 13:00:00+00:00 8.5627
2021-12-28 13:30:00+00:00 9.0500
2021-12-28 14:00:00+00:00 8.3857
2021-12-28 15:00:00+00:00 8.3764
2021-12-29 06:00:00+00:00 8.3671
2021-12-29 06:30:00+00:00 8.8800
2021-12-29 07:00:00+00:00 8.3483
2021-12-29 07:30:00+00:00 9.1200
2021-12-29 08:00:00+00:00 8.5721
2021-12-29 08:30:00+00:00 9.1800
2021-12-29 09:00:00+00:00 8.5999
2021-12-29 09:30:00+00:00 9.2700
2021-12-29 10:00:00+00:00 8.6559
2021-12-29 10:30:00+00:00 9.2300
2021-12-29 11:00:00+00:00 8.6373
2021-12-29 11:30:00+00:00 9.2800
2021-12-29 12:00:00+00:00 8.6186
2021-12-29 12:30:00+00:00 9.3600
price
2023-09-21 10:00:00+00:00 25.580000
2023-09-21 10:30:00+00:00 25.900000
2023-09-21 11:00:00+00:00 26.060000
2023-09-21 11:30:00+00:00 26.040001
2023-09-21 12:00:00+00:00 25.960000
2023-09-21 12:30:00+00:00 26.200001
2023-09-21 13:00:00+00:00 26.220000
2023-09-21 13:30:00+00:00 26.219999
2023-09-21 14:00:00+00:00 26.440000
2023-09-21 14:30:00+00:00 26.440001
2023-09-21 15:00:00+00:00 26.460000
2023-09-22 06:00:00+00:00 26.400000
2023-09-22 06:30:00+00:00 26.639999
2023-09-22 07:00:00+00:00 26.600000
2023-09-22 07:30:00+00:00 26.660000
2023-09-22 08:00:00+00:00 26.780000
2023-09-22 08:30:00+00:00 26.740000
2023-09-22 09:00:00+00:00 26.540000
2023-09-22 09:30:00+00:00 26.440001
2023-09-22 10:00:00+00:00 26.500000
2023-09-22 10:30:00+00:00 27.340000
2023-09-22 11:00:00+00:00 27.320000
2023-09-22 11:30:00+00:00 27.280001
2023-09-22 12:00:00+00:00 27.400000
2023-09-22 12:30:00+00:00 27.559999
2023-09-22 13:00:00+00:00 27.560000
2023-09-22 13:30:00+00:00 27.340000
2023-09-22 14:00:00+00:00 27.620000
2023-09-22 14:30:00+00:00 27.620001
2023-09-22 15:00:00+00:00 27.520000
Mean of the first 10 values: price 29.704
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 27.64
2023-09-25 10:00:00+03:00 28.60
2023-09-25 11:00:00+03:00 29.48
2023-09-25 12:00:00+03:00 29.76
2023-09-25 13:00:00+03:00 30.26
2023-09-25 14:00:00+03:00 30.26
2023-09-25 15:00:00+03:00 30.26
2023-09-25 16:00:00+03:00 30.26
2023-09-25 17:00:00+03:00 30.26
2023-09-25 18:00:00+03:00 30.26
price
2018-01-02 10:00:00+03:00 -1.660000e-02
2018-01-02 11:00:00+03:00 4.150000e-02
2018-01-02 12:00:00+03:00 -2.490000e-02
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 8.300000e-03
... ...
2023-09-22 13:00:00+00:00 5.340576e-07
2023-09-22 13:30:00+00:00 -2.199998e-01
2023-09-22 14:00:00+00:00 2.799998e-01
2023-09-22 14:30:00+00:00 8.392334e-07
2023-09-22 15:00:00+00:00 -1.000008e-01
[17890 rows x 1 columns]
ADF Statistic: -19.553223976995625
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17891
Model: ARIMA(30, 3, 1) Log Likelihood 8567.696
Date: Sun, 24 Dec 2023 AIC -17071.393
Time: 21:38:09 BIC -16822.052
Sample: 0 HQIC -16989.373
- 17891
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.4345 0.004 -319.177 0.000 -1.443 -1.426
ar.L2 -1.2996 0.010 -134.545 0.000 -1.318 -1.281
ar.L3 -1.3753 0.014 -99.202 0.000 -1.403 -1.348
ar.L4 -1.2968 0.018 -73.375 0.000 -1.331 -1.262
ar.L5 -1.3124 0.021 -62.971 0.000 -1.353 -1.272
ar.L6 -1.2405 0.024 -52.041 0.000 -1.287 -1.194
ar.L7 -1.1949 0.026 -45.585 0.000 -1.246 -1.144
ar.L8 -1.1233 0.028 -40.800 0.000 -1.177 -1.069
ar.L9 -1.0947 0.028 -38.523 0.000 -1.150 -1.039
ar.L10 -1.0458 0.030 -35.253 0.000 -1.104 -0.988
ar.L11 -1.0208 0.031 -33.275 0.000 -1.081 -0.961
ar.L12 -1.0066 0.031 -32.069 0.000 -1.068 -0.945
ar.L13 -0.9101 0.032 -28.630 0.000 -0.972 -0.848
ar.L14 -0.8829 0.032 -27.691 0.000 -0.945 -0.820
ar.L15 -0.8420 0.032 -26.350 0.000 -0.905 -0.779
ar.L16 -0.9008 0.031 -28.728 0.000 -0.962 -0.839
ar.L17 -0.9473 0.031 -30.827 0.000 -1.008 -0.887
ar.L18 -0.5339 0.030 -17.505 0.000 -0.594 -0.474
ar.L19 -0.3136 0.029 -10.796 0.000 -0.371 -0.257
ar.L20 -0.4760 0.027 -17.436 0.000 -0.530 -0.423
ar.L21 -0.4426 0.026 -16.936 0.000 -0.494 -0.391
ar.L22 -0.4320 0.025 -17.436 0.000 -0.481 -0.383
ar.L23 -0.3833 0.024 -16.168 0.000 -0.430 -0.337
ar.L24 -0.3191 0.022 -14.785 0.000 -0.361 -0.277
ar.L25 -0.2899 0.020 -14.618 0.000 -0.329 -0.251
ar.L26 -0.2068 0.018 -11.410 0.000 -0.242 -0.171
ar.L27 -0.1695 0.016 -10.326 0.000 -0.202 -0.137
ar.L28 -0.0790 0.014 -5.801 0.000 -0.106 -0.052
ar.L29 -0.0388 0.010 -3.746 0.000 -0.059 -0.018
ar.L30 0.0619 0.006 9.986 0.000 0.050 0.074
ma.L1 -0.9498 0.003 -291.510 0.000 -0.956 -0.943
sigma2 0.0224 9.22e-05 243.497 0.000 0.022 0.023
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 330911.07
Prob(Q): 0.91 Prob(JB): 0.00
Heteroskedasticity (H): 48.43 Skew: 0.24
Prob(H) (two-sided): 0.00 Kurtosis: 24.07
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891 27.721452
17892 27.777728
17893 27.796666
17894 28.000454
17895 28.020671
17896 28.044355
17897 27.959670
17898 28.077803
17899 28.488024
17900 28.519237
Name: predicted_mean, dtype: float64
lower price upper price
17891 27.427813 28.015091
17892 27.432896 28.122559
17893 27.349821 28.243512
17894 27.503916 28.496992
17895 27.437305 28.604038
17896 27.409055 28.679656
17897 27.239923 28.679418
17898 27.298213 28.857393
17899 27.621478 29.354571
17900 27.587817 29.450657
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 1.8099643456148864 Weighted Mean Absolute Percentage Error (WMAPE): 5.869630706095556
forecastplusyahoo('KCHOL',30,1,3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 28.3315 KCHOL
2021-12-27 10:00:00+03:00 28.7712 KCHOL
2021-12-27 11:00:00+03:00 28.6565 KCHOL
2021-12-27 12:00:00+03:00 28.6375 KCHOL
2021-12-27 13:00:00+03:00 28.6565 KCHOL
... ... ...
2023-09-22 14:00:00+03:00 138.8000 KCHOL
2023-09-22 15:00:00+03:00 138.9000 KCHOL
2023-09-22 16:00:00+03:00 139.0000 KCHOL
2023-09-22 17:00:00+03:00 137.3000 KCHOL
2023-09-22 18:00:00+03:00 137.5000 KCHOL
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for KCHOL.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 28.331500
2021-12-27 06:30:00+00:00 30.040001
2021-12-27 07:00:00+00:00 28.771200
2021-12-27 07:30:00+00:00 30.100000
2021-12-27 08:00:00+00:00 28.656500
2021-12-27 08:30:00+00:00 30.020000
2021-12-27 09:00:00+00:00 28.637500
2021-12-27 09:30:00+00:00 29.860001
2021-12-27 10:00:00+00:00 28.656500
2021-12-27 10:30:00+00:00 29.719999
2021-12-27 11:00:00+00:00 28.236000
2021-12-27 11:30:00+00:00 29.639999
2021-12-27 12:00:00+00:00 28.427200
2021-12-27 12:30:00+00:00 29.700001
2021-12-27 13:00:00+00:00 28.465400
2021-12-27 13:30:00+00:00 29.820000
2021-12-27 14:00:00+00:00 28.197700
2021-12-27 15:00:00+00:00 28.140400
2021-12-28 06:00:00+00:00 28.255000
2021-12-28 06:30:00+00:00 29.240000
2021-12-28 07:00:00+00:00 28.063900
2021-12-28 07:30:00+00:00 29.139999
2021-12-28 08:00:00+00:00 27.738900
2021-12-28 08:30:00+00:00 29.000000
2021-12-28 09:00:00+00:00 27.681500
2021-12-28 09:30:00+00:00 28.860001
2021-12-28 10:00:00+00:00 27.719800
2021-12-28 10:30:00+00:00 28.959999
2021-12-28 11:00:00+00:00 27.547700
2021-12-28 11:30:00+00:00 28.940001
2021-12-28 12:00:00+00:00 27.643300
2021-12-28 12:30:00+00:00 28.840000
2021-12-28 13:00:00+00:00 27.375700
2021-12-28 13:30:00+00:00 28.100000
2021-12-28 14:00:00+00:00 27.184500
2021-12-28 15:00:00+00:00 27.433000
2021-12-29 06:00:00+00:00 27.318400
2021-12-29 06:30:00+00:00 28.160000
2021-12-29 07:00:00+00:00 27.031600
2021-12-29 07:30:00+00:00 28.760000
2021-12-29 08:00:00+00:00 27.605100
2021-12-29 08:30:00+00:00 28.840000
2021-12-29 09:00:00+00:00 27.719800
2021-12-29 09:30:00+00:00 28.940001
2021-12-29 10:00:00+00:00 27.719800
2021-12-29 10:30:00+00:00 28.860001
2021-12-29 11:00:00+00:00 27.777200
2021-12-29 11:30:00+00:00 29.320000
2021-12-29 12:00:00+00:00 28.025700
2021-12-29 12:30:00+00:00 29.600000
price
2023-09-21 10:00:00+00:00 134.500000
2023-09-21 10:30:00+00:00 134.500000
2023-09-21 11:00:00+00:00 136.900000
2023-09-21 11:30:00+00:00 137.399994
2023-09-21 12:00:00+00:00 137.000000
2023-09-21 12:30:00+00:00 138.500000
2023-09-21 13:00:00+00:00 136.900000
2023-09-21 13:30:00+00:00 138.199997
2023-09-21 14:00:00+00:00 138.700000
2023-09-21 14:30:00+00:00 138.699997
2023-09-21 15:00:00+00:00 138.800000
2023-09-22 06:00:00+00:00 139.300000
2023-09-22 06:30:00+00:00 140.199997
2023-09-22 07:00:00+00:00 140.600000
2023-09-22 07:30:00+00:00 140.199997
2023-09-22 08:00:00+00:00 139.300000
2023-09-22 08:30:00+00:00 139.800003
2023-09-22 09:00:00+00:00 140.000000
2023-09-22 09:30:00+00:00 139.800003
2023-09-22 10:00:00+00:00 139.800000
2023-09-22 10:30:00+00:00 139.199997
2023-09-22 11:00:00+00:00 138.800000
2023-09-22 11:30:00+00:00 138.500000
2023-09-22 12:00:00+00:00 138.900000
2023-09-22 12:30:00+00:00 139.100006
2023-09-22 13:00:00+00:00 139.000000
2023-09-22 13:30:00+00:00 137.800003
2023-09-22 14:00:00+00:00 137.300000
2023-09-22 14:30:00+00:00 137.399994
2023-09-22 15:00:00+00:00 137.500000
Mean of the first 10 values: price 141.23
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 138.0
2023-09-25 10:00:00+03:00 139.8
2023-09-25 11:00:00+03:00 139.9
2023-09-25 12:00:00+03:00 141.2
2023-09-25 13:00:00+03:00 141.9
2023-09-25 14:00:00+03:00 142.2
2023-09-25 15:00:00+03:00 142.2
2023-09-25 16:00:00+03:00 142.8
2023-09-25 17:00:00+03:00 142.3
2023-09-25 18:00:00+03:00 142.0
price
2018-01-02 10:00:00+03:00 -0.026200
2018-01-02 11:00:00+03:00 0.252400
2018-01-02 12:00:00+03:00 -0.087000
2018-01-02 13:00:00+03:00 0.078200
2018-01-02 14:00:00+03:00 0.130500
... ...
2023-09-22 13:00:00+00:00 -0.100006
2023-09-22 13:30:00+00:00 -1.199997
2023-09-22 14:00:00+00:00 -0.500003
2023-09-22 14:30:00+00:00 0.099994
2023-09-22 15:00:00+00:00 0.100006
[17889 rows x 1 columns]
ADF Statistic: -20.86062436034284
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 3, 1) Log Likelihood -11538.687
Date: Sun, 24 Dec 2023 AIC 23141.374
Time: 21:42:00 BIC 23390.713
Sample: 0 HQIC 23223.393
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.3188 0.003 -420.000 0.000 -1.325 -1.313
ar.L2 -1.1751 0.005 -225.618 0.000 -1.185 -1.165
ar.L3 -1.2516 0.007 -176.232 0.000 -1.266 -1.238
ar.L4 -1.1343 0.009 -131.957 0.000 -1.151 -1.117
ar.L5 -1.1306 0.010 -113.863 0.000 -1.150 -1.111
ar.L6 -1.0223 0.011 -90.294 0.000 -1.045 -1.000
ar.L7 -1.0085 0.013 -80.556 0.000 -1.033 -0.984
ar.L8 -0.9694 0.013 -74.273 0.000 -0.995 -0.944
ar.L9 -0.9603 0.013 -71.867 0.000 -0.986 -0.934
ar.L10 -0.8597 0.014 -61.986 0.000 -0.887 -0.832
ar.L11 -0.7909 0.014 -56.440 0.000 -0.818 -0.763
ar.L12 -0.7569 0.014 -53.778 0.000 -0.784 -0.729
ar.L13 -0.7003 0.014 -49.492 0.000 -0.728 -0.673
ar.L14 -0.6733 0.013 -50.081 0.000 -0.700 -0.647
ar.L15 -0.6040 0.013 -45.932 0.000 -0.630 -0.578
ar.L16 -0.6054 0.013 -46.286 0.000 -0.631 -0.580
ar.L17 -0.6090 0.012 -49.128 0.000 -0.633 -0.585
ar.L18 -0.4062 0.012 -33.790 0.000 -0.430 -0.383
ar.L19 -0.3028 0.012 -24.884 0.000 -0.327 -0.279
ar.L20 -0.3849 0.012 -31.584 0.000 -0.409 -0.361
ar.L21 -0.3496 0.012 -29.115 0.000 -0.373 -0.326
ar.L22 -0.3328 0.012 -28.399 0.000 -0.356 -0.310
ar.L23 -0.2916 0.011 -25.451 0.000 -0.314 -0.269
ar.L24 -0.2413 0.011 -22.671 0.000 -0.262 -0.220
ar.L25 -0.2025 0.010 -21.150 0.000 -0.221 -0.184
ar.L26 -0.0917 0.009 -10.668 0.000 -0.109 -0.075
ar.L27 -0.0672 0.008 -8.340 0.000 -0.083 -0.051
ar.L28 -0.0145 0.007 -1.990 0.047 -0.029 -0.000
ar.L29 -0.0410 0.007 -5.958 0.000 -0.054 -0.028
ar.L30 0.0388 0.005 8.365 0.000 0.030 0.048
ma.L1 -0.9997 0.001 -734.949 0.000 -1.002 -0.997
sigma2 0.2116 0.001 285.044 0.000 0.210 0.213
===================================================================================
Ljung-Box (L1) (Q): 0.16 Jarque-Bera (JB): 580107.41
Prob(Q): 0.69 Prob(JB): 0.00
Heteroskedasticity (H): 45.60 Skew: -0.15
Prob(H) (two-sided): 0.00 Kurtosis: 30.90
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 137.709208
17891 137.788827
17892 137.606286
17893 137.502931
17894 137.490099
17895 137.916505
17896 137.454016
17897 137.748910
17898 137.483078
17899 137.513709
Name: predicted_mean, dtype: float64
lower price upper price
17890 136.807730 138.610687
17891 136.697914 138.879740
17892 136.232038 138.980535
17893 135.980475 139.025387
17894 135.735288 139.244910
17895 136.015791 139.817219
17896 135.331969 139.576063
17897 135.481144 140.016676
17898 135.017534 139.948623
17899 134.908361 140.119057
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 3.9015612302972906 Weighted Mean Absolute Percentage Error (WMAPE): 2.5551533958717365
forecastplusyahoo('KOZAL', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 4.9696 KOZAL
2021-12-27 10:00:00+03:00 5.0971 KOZAL
2021-12-27 11:00:00+03:00 5.0716 KOZAL
2021-12-27 12:00:00+03:00 5.0504 KOZAL
2021-12-27 13:00:00+03:00 5.0716 KOZAL
... ... ...
2023-09-22 14:00:00+03:00 28.1200 KOZAL
2023-09-22 15:00:00+03:00 28.0400 KOZAL
2023-09-22 16:00:00+03:00 28.0400 KOZAL
2023-09-22 17:00:00+03:00 27.9400 KOZAL
2023-09-22 18:00:00+03:00 28.0200 KOZAL
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for KOZAL.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 4.969600
2021-12-27 06:30:00+00:00 0.267347
2021-12-27 07:00:00+00:00 5.097100
2021-12-27 07:30:00+00:00 0.270748
2021-12-27 08:00:00+00:00 5.071600
2021-12-27 08:30:00+00:00 0.270748
2021-12-27 09:00:00+00:00 5.050400
2021-12-27 09:30:00+00:00 0.268254
2021-12-27 10:00:00+00:00 5.071600
2021-12-27 10:30:00+00:00 0.271202
2021-12-27 11:00:00+00:00 5.033400
2021-12-27 11:30:00+00:00 0.267800
2021-12-27 12:00:00+00:00 5.033400
2021-12-27 12:30:00+00:00 0.269388
2021-12-27 13:00:00+00:00 5.050400
2021-12-27 13:30:00+00:00 0.268481
2021-12-27 14:00:00+00:00 4.995200
2021-12-27 15:00:00+00:00 5.012100
2021-12-28 06:00:00+00:00 5.080200
2021-12-28 06:30:00+00:00 0.273696
2021-12-28 07:00:00+00:00 5.143900
2021-12-28 07:30:00+00:00 0.273016
2021-12-28 08:00:00+00:00 5.088700
2021-12-28 08:30:00+00:00 0.271429
2021-12-28 09:00:00+00:00 5.088700
2021-12-28 09:30:00+00:00 0.271655
2021-12-28 10:00:00+00:00 5.097100
2021-12-28 10:30:00+00:00 0.270748
2021-12-28 11:00:00+00:00 5.054700
2021-12-28 11:30:00+00:00 0.273016
2021-12-28 12:00:00+00:00 5.135500
2021-12-28 12:30:00+00:00 0.274150
2021-12-28 13:00:00+00:00 5.105700
2021-12-28 13:30:00+00:00 0.267574
2021-12-28 14:00:00+00:00 5.071600
2021-12-28 15:00:00+00:00 5.084400
2021-12-29 06:00:00+00:00 5.084400
2021-12-29 06:30:00+00:00 0.266667
2021-12-29 07:00:00+00:00 4.999400
2021-12-29 07:30:00+00:00 0.268027
2021-12-29 08:00:00+00:00 5.020700
2021-12-29 08:30:00+00:00 0.266893
2021-12-29 09:00:00+00:00 5.024900
2021-12-29 09:30:00+00:00 0.267574
2021-12-29 10:00:00+00:00 5.020700
2021-12-29 10:30:00+00:00 0.266667
2021-12-29 11:00:00+00:00 5.020700
2021-12-29 11:30:00+00:00 0.268027
2021-12-29 12:00:00+00:00 5.037600
2021-12-29 12:30:00+00:00 0.270068
price
2023-09-21 10:00:00+00:00 27.200000
2023-09-21 10:30:00+00:00 27.780001
2023-09-21 11:00:00+00:00 27.820000
2023-09-21 11:30:00+00:00 27.799999
2023-09-21 12:00:00+00:00 27.720000
2023-09-21 12:30:00+00:00 28.120001
2023-09-21 13:00:00+00:00 28.000000
2023-09-21 13:30:00+00:00 28.080000
2023-09-21 14:00:00+00:00 28.460000
2023-09-21 14:30:00+00:00 28.459999
2023-09-21 15:00:00+00:00 28.600000
2023-09-22 06:00:00+00:00 28.780000
2023-09-22 06:30:00+00:00 28.520000
2023-09-22 07:00:00+00:00 28.460000
2023-09-22 07:30:00+00:00 28.400000
2023-09-22 08:00:00+00:00 28.440000
2023-09-22 08:30:00+00:00 28.320000
2023-09-22 09:00:00+00:00 28.300000
2023-09-22 09:30:00+00:00 28.260000
2023-09-22 10:00:00+00:00 28.260000
2023-09-22 10:30:00+00:00 28.219999
2023-09-22 11:00:00+00:00 28.120000
2023-09-22 11:30:00+00:00 28.020000
2023-09-22 12:00:00+00:00 28.040000
2023-09-22 12:30:00+00:00 28.320000
2023-09-22 13:00:00+00:00 28.040000
2023-09-22 13:30:00+00:00 27.860001
2023-09-22 14:00:00+00:00 27.940000
2023-09-22 14:30:00+00:00 27.940001
2023-09-22 15:00:00+00:00 28.020000
Mean of the first 10 values: price 28.638
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 28.22
2023-09-25 10:00:00+03:00 28.38
2023-09-25 11:00:00+03:00 28.54
2023-09-25 12:00:00+03:00 28.58
2023-09-25 13:00:00+03:00 28.60
2023-09-25 14:00:00+03:00 28.68
2023-09-25 15:00:00+03:00 28.54
2023-09-25 16:00:00+03:00 28.88
2023-09-25 17:00:00+03:00 28.96
2023-09-25 18:00:00+03:00 29.00
price
2018-01-02 10:00:00+03:00 2.040000e-02
2018-01-02 11:00:00+03:00 -5.100000e-03
2018-01-02 12:00:00+03:00 -3.400000e-03
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 1.440000e-02
... ...
2023-09-22 13:00:00+00:00 -2.799997e-01
2023-09-22 13:30:00+00:00 -1.799994e-01
2023-09-22 14:00:00+00:00 7.999939e-02
2023-09-22 14:30:00+00:00 5.340576e-07
2023-09-22 15:00:00+00:00 7.999947e-02
[17889 rows x 1 columns]
ADF Statistic: -19.812067236570215
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 1, 1) Log Likelihood -24459.787
Date: Sun, 24 Dec 2023 AIC 48983.574
Time: 21:44:33 BIC 49232.916
Sample: 0 HQIC 49065.594
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.6103 0.033 -18.377 0.000 -0.675 -0.545
ar.L2 0.3376 0.049 6.853 0.000 0.241 0.434
ar.L3 0.1726 0.032 5.418 0.000 0.110 0.235
ar.L4 0.0346 0.021 1.642 0.101 -0.007 0.076
ar.L5 -0.0061 0.017 -0.359 0.720 -0.040 0.027
ar.L6 0.0416 0.016 2.560 0.010 0.010 0.073
ar.L7 -0.0090 0.012 -0.729 0.466 -0.033 0.015
ar.L8 0.0022 0.011 0.196 0.844 -0.020 0.024
ar.L9 -0.0221 0.011 -1.944 0.052 -0.044 0.000
ar.L10 0.0082 0.011 0.734 0.463 -0.014 0.030
ar.L11 -0.0317 0.008 -3.759 0.000 -0.048 -0.015
ar.L12 -0.1005 0.009 -11.439 0.000 -0.118 -0.083
ar.L13 -0.0544 0.011 -4.960 0.000 -0.076 -0.033
ar.L14 -0.0685 0.011 -6.371 0.000 -0.090 -0.047
ar.L15 -0.0189 0.011 -1.693 0.090 -0.041 0.003
ar.L16 -0.1105 0.011 -10.409 0.000 -0.131 -0.090
ar.L17 0.0134 0.013 1.056 0.291 -0.011 0.038
ar.L18 0.7772 0.010 75.208 0.000 0.757 0.797
ar.L19 0.5681 0.017 32.620 0.000 0.534 0.602
ar.L20 -0.3564 0.034 -10.429 0.000 -0.423 -0.289
ar.L21 -0.2714 0.018 -14.931 0.000 -0.307 -0.236
ar.L22 -0.0520 0.007 -7.525 0.000 -0.066 -0.038
ar.L23 -0.0649 0.005 -14.041 0.000 -0.074 -0.056
ar.L24 -0.0530 0.004 -12.750 0.000 -0.061 -0.045
ar.L25 -0.0637 0.004 -16.143 0.000 -0.071 -0.056
ar.L26 -0.0181 0.006 -2.992 0.003 -0.030 -0.006
ar.L27 -0.0485 0.007 -6.750 0.000 -0.063 -0.034
ar.L28 -0.0258 0.008 -3.371 0.001 -0.041 -0.011
ar.L29 -0.0452 0.008 -5.485 0.000 -0.061 -0.029
ar.L30 0.0725 0.009 8.421 0.000 0.056 0.089
ma.L1 -0.8643 0.034 -25.791 0.000 -0.930 -0.799
sigma2 0.9016 0.002 410.194 0.000 0.897 0.906
===================================================================================
Ljung-Box (L1) (Q): 0.03 Jarque-Bera (JB): 26885429.13
Prob(Q): 0.87 Prob(JB): 0.00
Heteroskedasticity (H): 896.52 Skew: 5.27
Prob(H) (two-sided): 0.00 Kurtosis: 192.63
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 28.152962
17891 27.811522
17892 28.015828
17893 27.859128
17894 27.928055
17895 27.796296
17896 27.850601
17897 27.810418
17898 27.884050
17899 27.754271
Name: predicted_mean, dtype: float64
lower price upper price
17890 26.291935 30.013989
17891 25.751531 29.871512
17892 25.513935 30.517720
17893 25.288383 30.429873
17894 25.168078 30.688033
17895 25.013344 30.579247
17896 24.943880 30.757322
17897 24.889329 30.731507
17898 24.865659 30.902441
17899 24.724415 30.784126
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.817514809053018 Weighted Mean Absolute Percentage Error (WMAPE): 2.624788856189999
forecastplusyahoo('KOZAA', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 17.76 KOZAA
2021-12-27 10:00:00+03:00 18.25 KOZAA
2021-12-27 11:00:00+03:00 18.04 KOZAA
2021-12-27 12:00:00+03:00 17.92 KOZAA
2021-12-27 13:00:00+03:00 18.14 KOZAA
... ... ...
2023-09-22 14:00:00+03:00 64.60 KOZAA
2023-09-22 15:00:00+03:00 64.60 KOZAA
2023-09-22 16:00:00+03:00 64.45 KOZAA
2023-09-22 17:00:00+03:00 64.15 KOZAA
2023-09-22 18:00:00+03:00 64.45 KOZAA
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for KOZAA.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 17.760000
2021-12-27 06:30:00+00:00 17.959999
2021-12-27 07:00:00+00:00 18.250000
2021-12-27 07:30:00+00:00 18.129999
2021-12-27 08:00:00+00:00 18.040000
2021-12-27 08:30:00+00:00 18.040001
2021-12-27 09:00:00+00:00 17.920000
2021-12-27 09:30:00+00:00 17.830000
2021-12-27 10:00:00+00:00 18.140000
2021-12-27 10:30:00+00:00 18.280001
2021-12-27 11:00:00+00:00 18.110000
2021-12-27 11:30:00+00:00 17.900000
2021-12-27 12:00:00+00:00 18.000000
2021-12-27 12:30:00+00:00 18.010000
2021-12-27 13:00:00+00:00 18.040000
2021-12-27 13:30:00+00:00 18.110001
2021-12-27 14:00:00+00:00 17.930000
2021-12-27 15:00:00+00:00 17.940000
2021-12-28 06:00:00+00:00 18.160000
2021-12-28 06:30:00+00:00 18.160000
2021-12-28 07:00:00+00:00 18.330000
2021-12-28 07:30:00+00:00 18.129999
2021-12-28 08:00:00+00:00 18.060000
2021-12-28 08:30:00+00:00 18.080000
2021-12-28 09:00:00+00:00 18.060000
2021-12-28 09:30:00+00:00 18.030001
2021-12-28 10:00:00+00:00 18.040000
2021-12-28 10:30:00+00:00 17.930000
2021-12-28 11:00:00+00:00 17.910000
2021-12-28 11:30:00+00:00 18.160000
2021-12-28 12:00:00+00:00 18.200000
2021-12-28 12:30:00+00:00 18.200001
2021-12-28 13:00:00+00:00 18.000000
2021-12-28 13:30:00+00:00 17.709999
2021-12-28 14:00:00+00:00 17.720000
2021-12-28 15:00:00+00:00 17.770000
2021-12-29 06:00:00+00:00 17.720000
2021-12-29 06:30:00+00:00 17.590000
2021-12-29 07:00:00+00:00 17.570000
2021-12-29 07:30:00+00:00 17.750000
2021-12-29 08:00:00+00:00 17.770000
2021-12-29 08:30:00+00:00 17.719999
2021-12-29 09:00:00+00:00 17.770000
2021-12-29 09:30:00+00:00 17.780001
2021-12-29 10:00:00+00:00 17.870000
2021-12-29 10:30:00+00:00 17.719999
2021-12-29 11:00:00+00:00 17.840000
2021-12-29 11:30:00+00:00 17.950001
2021-12-29 12:00:00+00:00 17.990000
2021-12-29 12:30:00+00:00 18.120001
price
2023-09-21 10:00:00+00:00 63.550000
2023-09-21 10:30:00+00:00 65.199997
2023-09-21 11:00:00+00:00 65.250000
2023-09-21 11:30:00+00:00 65.099998
2023-09-21 12:00:00+00:00 65.050000
2023-09-21 12:30:00+00:00 66.050003
2023-09-21 13:00:00+00:00 65.850000
2023-09-21 13:30:00+00:00 66.050003
2023-09-21 14:00:00+00:00 66.750000
2023-09-21 14:30:00+00:00 66.750000
2023-09-21 15:00:00+00:00 66.650000
2023-09-22 06:00:00+00:00 65.800000
2023-09-22 06:30:00+00:00 65.900002
2023-09-22 07:00:00+00:00 65.900000
2023-09-22 07:30:00+00:00 65.750000
2023-09-22 08:00:00+00:00 66.000000
2023-09-22 08:30:00+00:00 65.449997
2023-09-22 09:00:00+00:00 65.400000
2023-09-22 09:30:00+00:00 65.099998
2023-09-22 10:00:00+00:00 65.200000
2023-09-22 10:30:00+00:00 64.800003
2023-09-22 11:00:00+00:00 64.600000
2023-09-22 11:30:00+00:00 64.449997
2023-09-22 12:00:00+00:00 64.600000
2023-09-22 12:30:00+00:00 65.000000
2023-09-22 13:00:00+00:00 64.450000
2023-09-22 13:30:00+00:00 64.150002
2023-09-22 14:00:00+00:00 64.150000
2023-09-22 14:30:00+00:00 64.150002
2023-09-22 15:00:00+00:00 64.450000
Mean of the first 10 values: price 65.285
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 65.30
2023-09-25 10:00:00+03:00 65.15
2023-09-25 11:00:00+03:00 65.00
2023-09-25 12:00:00+03:00 65.20
2023-09-25 13:00:00+03:00 65.55
2023-09-25 14:00:00+03:00 65.60
2023-09-25 15:00:00+03:00 64.75
2023-09-25 16:00:00+03:00 65.25
2023-09-25 17:00:00+03:00 65.50
2023-09-25 18:00:00+03:00 65.55
price
2018-01-02 10:00:00+03:00 0.020000
2018-01-02 11:00:00+03:00 -0.030000
2018-01-02 12:00:00+03:00 -0.020000
2018-01-02 13:00:00+03:00 0.000000
2018-01-02 14:00:00+03:00 0.020000
... ...
2023-09-22 13:00:00+00:00 -0.550000
2023-09-22 13:30:00+00:00 -0.299998
2023-09-22 14:00:00+00:00 -0.000002
2023-09-22 14:30:00+00:00 0.000002
2023-09-22 15:00:00+00:00 0.299998
[17889 rows x 1 columns]
ADF Statistic: -19.13934986413561
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 3, 1) Log Likelihood -1801.296
Date: Sun, 24 Dec 2023 AIC 3666.593
Time: 21:55:42 BIC 3915.931
Sample: 0 HQIC 3748.612
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.9633 0.003 -309.784 0.000 -0.969 -0.957
ar.L2 -0.9478 0.004 -217.776 0.000 -0.956 -0.939
ar.L3 -0.9094 0.006 -148.910 0.000 -0.921 -0.897
ar.L4 -0.8850 0.007 -125.340 0.000 -0.899 -0.871
ar.L5 -0.8482 0.008 -101.416 0.000 -0.865 -0.832
ar.L6 -0.8448 0.010 -88.119 0.000 -0.864 -0.826
ar.L7 -0.8126 0.010 -80.371 0.000 -0.832 -0.793
ar.L8 -0.7759 0.011 -73.389 0.000 -0.797 -0.755
ar.L9 -0.7314 0.011 -66.506 0.000 -0.753 -0.710
ar.L10 -0.7053 0.012 -59.090 0.000 -0.729 -0.682
ar.L11 -0.6518 0.012 -52.993 0.000 -0.676 -0.628
ar.L12 -0.6209 0.013 -49.075 0.000 -0.646 -0.596
ar.L13 -0.5997 0.013 -46.854 0.000 -0.625 -0.575
ar.L14 -0.5304 0.013 -41.228 0.000 -0.556 -0.505
ar.L15 -0.4973 0.013 -38.919 0.000 -0.522 -0.472
ar.L16 -0.4701 0.013 -36.809 0.000 -0.495 -0.445
ar.L17 -0.4182 0.012 -33.562 0.000 -0.443 -0.394
ar.L18 -0.3792 0.012 -30.958 0.000 -0.403 -0.355
ar.L19 -0.3566 0.012 -29.992 0.000 -0.380 -0.333
ar.L20 -0.3590 0.011 -31.303 0.000 -0.381 -0.336
ar.L21 -0.3655 0.011 -31.950 0.000 -0.388 -0.343
ar.L22 -0.3543 0.011 -32.079 0.000 -0.376 -0.333
ar.L23 -0.3059 0.011 -27.864 0.000 -0.327 -0.284
ar.L24 -0.2709 0.010 -25.841 0.000 -0.291 -0.250
ar.L25 -0.2385 0.010 -24.341 0.000 -0.258 -0.219
ar.L26 -0.2035 0.009 -22.816 0.000 -0.221 -0.186
ar.L27 -0.1653 0.008 -21.126 0.000 -0.181 -0.150
ar.L28 -0.1119 0.007 -15.990 0.000 -0.126 -0.098
ar.L29 -0.0768 0.006 -13.178 0.000 -0.088 -0.065
ar.L30 -0.0597 0.004 -15.233 0.000 -0.067 -0.052
ma.L1 -0.9999 0.002 -531.634 0.000 -1.004 -0.996
sigma2 0.0715 0.000 323.364 0.000 0.071 0.072
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 680920.91
Prob(Q): 0.94 Prob(JB): 0.00
Heteroskedasticity (H): 24.91 Skew: 1.13
Prob(H) (two-sided): 0.00 Kurtosis: 33.14
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 64.442217
17891 64.499146
17892 64.617654
17893 64.631578
17894 64.573736
17895 64.535947
17896 64.562677
17897 64.513164
17898 64.499930
17899 64.433314
Name: predicted_mean, dtype: float64
lower price upper price
17890 63.917981 64.966453
17891 63.744000 65.254293
17892 63.682013 65.553295
17893 63.534279 65.728877
17894 63.328933 65.818538
17895 63.150251 65.921642
17896 63.047298 66.078057
17897 62.871170 66.155159
17898 62.732143 66.267716
17899 62.538650 66.327979
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.808649516198752 Weighted Mean Absolute Percentage Error (WMAPE): 1.1550336196433288
forecastplusyahoo('PGSUS',30,1,3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 89.90 PGSUS
2021-12-27 10:00:00+03:00 91.65 PGSUS
2021-12-27 11:00:00+03:00 90.35 PGSUS
2021-12-27 12:00:00+03:00 90.15 PGSUS
2021-12-27 13:00:00+03:00 90.30 PGSUS
... ... ...
2023-09-22 14:00:00+03:00 762.50 PGSUS
2023-09-22 15:00:00+03:00 761.60 PGSUS
2023-09-22 16:00:00+03:00 761.40 PGSUS
2023-09-22 17:00:00+03:00 766.20 PGSUS
2023-09-22 18:00:00+03:00 767.20 PGSUS
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for PGSUS.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 89.900000
2021-12-27 06:30:00+00:00 90.400002
2021-12-27 07:00:00+00:00 91.650000
2021-12-27 07:30:00+00:00 90.250000
2021-12-27 08:00:00+00:00 90.350000
2021-12-27 08:30:00+00:00 90.599998
2021-12-27 09:00:00+00:00 90.150000
2021-12-27 09:30:00+00:00 89.699997
2021-12-27 10:00:00+00:00 90.300000
2021-12-27 10:30:00+00:00 90.099998
2021-12-27 11:00:00+00:00 88.950000
2021-12-27 11:30:00+00:00 89.000000
2021-12-27 12:00:00+00:00 89.050000
2021-12-27 12:30:00+00:00 88.550003
2021-12-27 13:00:00+00:00 88.900000
2021-12-27 13:30:00+00:00 88.650002
2021-12-27 14:00:00+00:00 88.100000
2021-12-27 15:00:00+00:00 87.900000
2021-12-28 06:00:00+00:00 89.000000
2021-12-28 06:30:00+00:00 87.949997
2021-12-28 07:00:00+00:00 88.700000
2021-12-28 07:30:00+00:00 88.750000
2021-12-28 08:00:00+00:00 88.050000
2021-12-28 08:30:00+00:00 88.050003
2021-12-28 09:00:00+00:00 88.050000
2021-12-28 09:30:00+00:00 88.150002
2021-12-28 10:00:00+00:00 87.850000
2021-12-28 10:30:00+00:00 87.400002
2021-12-28 11:00:00+00:00 87.300000
2021-12-28 11:30:00+00:00 87.449997
2021-12-28 12:00:00+00:00 87.350000
2021-12-28 12:30:00+00:00 86.849998
2021-12-28 13:00:00+00:00 86.050000
2021-12-28 13:30:00+00:00 84.849998
2021-12-28 14:00:00+00:00 86.600000
2021-12-28 15:00:00+00:00 86.300000
2021-12-29 06:00:00+00:00 85.750000
2021-12-29 06:30:00+00:00 84.500000
2021-12-29 07:00:00+00:00 84.500000
2021-12-29 07:30:00+00:00 85.800003
2021-12-29 08:00:00+00:00 87.250000
2021-12-29 08:30:00+00:00 86.650002
2021-12-29 09:00:00+00:00 87.250000
2021-12-29 09:30:00+00:00 86.250000
2021-12-29 10:00:00+00:00 87.000000
2021-12-29 10:30:00+00:00 86.500000
2021-12-29 11:00:00+00:00 87.350000
2021-12-29 11:30:00+00:00 87.750000
2021-12-29 12:00:00+00:00 87.750000
2021-12-29 12:30:00+00:00 88.349998
price
2023-09-21 10:00:00+00:00 746.400000
2023-09-21 10:30:00+00:00 756.099976
2023-09-21 11:00:00+00:00 764.300000
2023-09-21 11:30:00+00:00 760.000000
2023-09-21 12:00:00+00:00 756.800000
2023-09-21 12:30:00+00:00 765.000000
2023-09-21 13:00:00+00:00 763.700000
2023-09-21 13:30:00+00:00 768.099976
2023-09-21 14:00:00+00:00 774.100000
2023-09-21 14:30:00+00:00 774.099976
2023-09-21 15:00:00+00:00 772.800000
2023-09-22 06:00:00+00:00 779.800000
2023-09-22 06:30:00+00:00 775.500000
2023-09-22 07:00:00+00:00 769.500000
2023-09-22 07:30:00+00:00 774.400024
2023-09-22 08:00:00+00:00 774.700000
2023-09-22 08:30:00+00:00 773.700012
2023-09-22 09:00:00+00:00 773.200000
2023-09-22 09:30:00+00:00 771.900024
2023-09-22 10:00:00+00:00 770.600000
2023-09-22 10:30:00+00:00 766.299988
2023-09-22 11:00:00+00:00 762.500000
2023-09-22 11:30:00+00:00 759.799988
2023-09-22 12:00:00+00:00 761.600000
2023-09-22 12:30:00+00:00 762.700012
2023-09-22 13:00:00+00:00 761.400000
2023-09-22 13:30:00+00:00 759.500000
2023-09-22 14:00:00+00:00 766.200000
2023-09-22 14:30:00+00:00 766.200012
2023-09-22 15:00:00+00:00 767.200000
Mean of the first 10 values: price 781.74
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 770.0
2023-09-25 10:00:00+03:00 763.0
2023-09-25 11:00:00+03:00 772.6
2023-09-25 12:00:00+03:00 781.6
2023-09-25 13:00:00+03:00 782.8
2023-09-25 14:00:00+03:00 778.9
2023-09-25 15:00:00+03:00 780.1
2023-09-25 16:00:00+03:00 787.4
2023-09-25 17:00:00+03:00 801.0
2023-09-25 18:00:00+03:00 800.0
price
2018-01-02 10:00:00+03:00 0.300000
2018-01-02 11:00:00+03:00 0.740000
2018-01-02 12:00:00+03:00 0.080000
2018-01-02 13:00:00+03:00 -0.020000
2018-01-02 14:00:00+03:00 0.200000
... ...
2023-09-22 13:00:00+00:00 -1.300012
2023-09-22 13:30:00+00:00 -1.900000
2023-09-22 14:00:00+00:00 6.700000
2023-09-22 14:30:00+00:00 0.000012
2023-09-22 15:00:00+00:00 0.999988
[17885 rows x 1 columns]
ADF Statistic: -21.812130999552586
p-value: 0.0
Critical Values: {'1%': -3.4307165848952463, '5%': -2.861702016559853, '10%': -2.566856237175401}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17886
Model: ARIMA(30, 3, 1) Log Likelihood -39762.564
Date: Sun, 24 Dec 2023 AIC 79589.127
Time: 21:51:21 BIC 79838.459
Sample: 0 HQIC 79671.145
- 17886
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.9895 0.002 -423.444 0.000 -0.994 -0.985
ar.L2 -0.9942 0.004 -223.995 0.000 -1.003 -0.985
ar.L3 -0.9731 0.006 -154.602 0.000 -0.985 -0.961
ar.L4 -0.9366 0.007 -125.386 0.000 -0.951 -0.922
ar.L5 -0.8962 0.008 -106.130 0.000 -0.913 -0.880
ar.L6 -0.8418 0.009 -94.583 0.000 -0.859 -0.824
ar.L7 -0.8049 0.010 -83.596 0.000 -0.824 -0.786
ar.L8 -0.7667 0.010 -74.840 0.000 -0.787 -0.747
ar.L9 -0.7213 0.011 -67.078 0.000 -0.742 -0.700
ar.L10 -0.6640 0.011 -59.806 0.000 -0.686 -0.642
ar.L11 -0.6273 0.011 -54.570 0.000 -0.650 -0.605
ar.L12 -0.5978 0.012 -50.768 0.000 -0.621 -0.575
ar.L13 -0.5500 0.012 -45.925 0.000 -0.574 -0.527
ar.L14 -0.5100 0.012 -41.828 0.000 -0.534 -0.486
ar.L15 -0.4930 0.012 -40.464 0.000 -0.517 -0.469
ar.L16 -0.4542 0.012 -37.114 0.000 -0.478 -0.430
ar.L17 -0.4095 0.013 -32.137 0.000 -0.434 -0.385
ar.L18 -0.3555 0.012 -28.638 0.000 -0.380 -0.331
ar.L19 -0.3039 0.012 -24.850 0.000 -0.328 -0.280
ar.L20 -0.3183 0.012 -27.110 0.000 -0.341 -0.295
ar.L21 -0.3290 0.011 -29.255 0.000 -0.351 -0.307
ar.L22 -0.3322 0.011 -30.735 0.000 -0.353 -0.311
ar.L23 -0.2885 0.010 -27.691 0.000 -0.309 -0.268
ar.L24 -0.2635 0.010 -26.293 0.000 -0.283 -0.244
ar.L25 -0.1929 0.009 -20.398 0.000 -0.211 -0.174
ar.L26 -0.1336 0.009 -15.555 0.000 -0.150 -0.117
ar.L27 -0.0952 0.008 -12.129 0.000 -0.111 -0.080
ar.L28 -0.0823 0.007 -11.918 0.000 -0.096 -0.069
ar.L29 -0.0570 0.005 -11.042 0.000 -0.067 -0.047
ar.L30 -0.0336 0.003 -9.859 0.000 -0.040 -0.027
ma.L1 -1.0000 0.007 -140.925 0.000 -1.014 -0.986
sigma2 4.9906 0.032 154.919 0.000 4.927 5.054
===================================================================================
Ljung-Box (L1) (Q): 0.02 Jarque-Bera (JB): 5895945.35
Prob(Q): 0.88 Prob(JB): 0.00
Heteroskedasticity (H): 53.76 Skew: 2.92
Prob(H) (two-sided): 0.00 Kurtosis: 91.76
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17886 767.567744
17887 767.897003
17888 768.604571
17889 769.655875
17890 770.179944
17891 770.117911
17892 770.483798
17893 770.867781
17894 770.692424
17895 770.562294
Name: predicted_mean, dtype: float64
lower price upper price
17886 763.189170 771.946319
17887 761.671871 774.122135
17888 760.978522 776.230621
17889 760.802568 778.509182
17890 760.175417 780.184471
17891 759.004624 781.231198
17892 758.263741 782.703856
17893 757.566207 784.169355
17894 756.323032 785.061816
17895 755.118173 786.006416
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 16.04938089961804 Weighted Mean Absolute Percentage Error (WMAPE): 1.722789594582363
forecastplusyahoo('PETKM', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 8.15 PETKM
2021-12-27 10:00:00+03:00 8.39 PETKM
2021-12-27 11:00:00+03:00 8.25 PETKM
2021-12-27 12:00:00+03:00 8.19 PETKM
2021-12-27 13:00:00+03:00 8.18 PETKM
... ... ...
2023-09-22 14:00:00+03:00 19.75 PETKM
2023-09-22 15:00:00+03:00 19.77 PETKM
2023-09-22 16:00:00+03:00 19.80 PETKM
2023-09-22 17:00:00+03:00 19.95 PETKM
2023-09-22 18:00:00+03:00 19.90 PETKM
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for PETKM.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 8.15
2021-12-27 06:30:00+00:00 8.28
2021-12-27 07:00:00+00:00 8.39
2021-12-27 07:30:00+00:00 8.26
2021-12-27 08:00:00+00:00 8.25
2021-12-27 08:30:00+00:00 8.22
2021-12-27 09:00:00+00:00 8.19
2021-12-27 09:30:00+00:00 8.15
2021-12-27 10:00:00+00:00 8.18
2021-12-27 10:30:00+00:00 8.16
2021-12-27 11:00:00+00:00 8.12
2021-12-27 11:30:00+00:00 8.02
2021-12-27 12:00:00+00:00 8.06
2021-12-27 12:30:00+00:00 8.04
2021-12-27 13:00:00+00:00 8.06
2021-12-27 13:30:00+00:00 8.11
2021-12-27 14:00:00+00:00 8.01
2021-12-27 15:00:00+00:00 8.01
2021-12-28 06:00:00+00:00 8.12
2021-12-28 06:30:00+00:00 8.04
2021-12-28 07:00:00+00:00 8.06
2021-12-28 07:30:00+00:00 8.01
2021-12-28 08:00:00+00:00 7.97
2021-12-28 08:30:00+00:00 7.97
2021-12-28 09:00:00+00:00 7.93
2021-12-28 09:30:00+00:00 7.96
2021-12-28 10:00:00+00:00 7.95
2021-12-28 10:30:00+00:00 7.93
2021-12-28 11:00:00+00:00 7.88
2021-12-28 11:30:00+00:00 7.92
2021-12-28 12:00:00+00:00 7.92
2021-12-28 12:30:00+00:00 7.93
2021-12-28 13:00:00+00:00 7.90
2021-12-28 13:30:00+00:00 7.72
2021-12-28 14:00:00+00:00 7.84
2021-12-28 15:00:00+00:00 7.82
2021-12-29 06:00:00+00:00 7.73
2021-12-29 06:30:00+00:00 7.72
2021-12-29 07:00:00+00:00 7.77
2021-12-29 07:30:00+00:00 7.85
2021-12-29 08:00:00+00:00 7.86
2021-12-29 08:30:00+00:00 7.87
2021-12-29 09:00:00+00:00 7.88
2021-12-29 09:30:00+00:00 7.89
2021-12-29 10:00:00+00:00 7.94
2021-12-29 10:30:00+00:00 7.89
2021-12-29 11:00:00+00:00 7.95
2021-12-29 11:30:00+00:00 7.99
2021-12-29 12:00:00+00:00 7.99
2021-12-29 12:30:00+00:00 8.05
price
2023-09-21 10:00:00+00:00 18.740000
2023-09-21 10:30:00+00:00 19.030001
2023-09-21 11:00:00+00:00 19.180000
2023-09-21 11:30:00+00:00 19.160000
2023-09-21 12:00:00+00:00 19.140000
2023-09-21 12:30:00+00:00 19.430000
2023-09-21 13:00:00+00:00 19.430000
2023-09-21 13:30:00+00:00 19.570000
2023-09-21 14:00:00+00:00 19.690000
2023-09-21 14:30:00+00:00 19.690001
2023-09-21 15:00:00+00:00 19.690000
2023-09-22 06:00:00+00:00 19.700000
2023-09-22 06:30:00+00:00 19.980000
2023-09-22 07:00:00+00:00 20.080000
2023-09-22 07:30:00+00:00 20.059999
2023-09-22 08:00:00+00:00 20.160000
2023-09-22 08:30:00+00:00 20.020000
2023-09-22 09:00:00+00:00 20.040000
2023-09-22 09:30:00+00:00 20.020000
2023-09-22 10:00:00+00:00 19.910000
2023-09-22 10:30:00+00:00 19.670000
2023-09-22 11:00:00+00:00 19.750000
2023-09-22 11:30:00+00:00 19.650000
2023-09-22 12:00:00+00:00 19.770000
2023-09-22 12:30:00+00:00 19.809999
2023-09-22 13:00:00+00:00 19.800000
2023-09-22 13:30:00+00:00 19.709999
2023-09-22 14:00:00+00:00 19.950000
2023-09-22 14:30:00+00:00 19.950001
2023-09-22 15:00:00+00:00 19.900000
Mean of the first 10 values: price 20.306
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 20.10
2023-09-25 10:00:00+03:00 20.22
2023-09-25 11:00:00+03:00 20.26
2023-09-25 12:00:00+03:00 20.26
2023-09-25 13:00:00+03:00 20.40
2023-09-25 14:00:00+03:00 20.30
2023-09-25 15:00:00+03:00 20.34
2023-09-25 16:00:00+03:00 20.40
2023-09-25 17:00:00+03:00 20.40
2023-09-25 18:00:00+03:00 20.38
price
2018-01-02 10:00:00+03:00 3.380000e-02
2018-01-02 11:00:00+03:00 1.120000e-02
2018-01-02 12:00:00+03:00 5.600000e-03
2018-01-02 13:00:00+03:00 5.600000e-03
2018-01-02 14:00:00+03:00 2.820000e-02
... ...
2023-09-22 13:00:00+00:00 -9.999466e-03
2023-09-22 13:30:00+00:00 -9.000092e-02
2023-09-22 14:00:00+00:00 2.400009e-01
2023-09-22 14:30:00+00:00 7.629395e-07
2023-09-22 15:00:00+00:00 -5.000076e-02
[17890 rows x 1 columns]
ADF Statistic: -20.941724628552823
p-value: 0.0
Critical Values: {'1%': -3.430716400035936, '5%': -2.8617019348641204, '10%': -2.566856193690025}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17891
Model: ARIMA(30, 3, 1) Log Likelihood 21557.981
Date: Sun, 24 Dec 2023 AIC -43051.962
Time: 21:58:43 BIC -42802.622
Sample: 0 HQIC -42969.943
- 17891
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.0301 0.004 -246.202 0.000 -1.038 -1.022
ar.L2 -0.9590 0.007 -137.228 0.000 -0.973 -0.945
ar.L3 -0.9755 0.010 -100.424 0.000 -0.995 -0.956
ar.L4 -0.9128 0.012 -77.518 0.000 -0.936 -0.890
ar.L5 -0.9239 0.014 -68.419 0.000 -0.950 -0.897
ar.L6 -0.8403 0.015 -55.136 0.000 -0.870 -0.810
ar.L7 -0.8553 0.016 -52.087 0.000 -0.887 -0.823
ar.L8 -0.7804 0.017 -44.685 0.000 -0.815 -0.746
ar.L9 -0.7662 0.018 -42.283 0.000 -0.802 -0.731
ar.L10 -0.7117 0.019 -37.900 0.000 -0.749 -0.675
ar.L11 -0.6563 0.019 -34.040 0.000 -0.694 -0.619
ar.L12 -0.6369 0.019 -33.126 0.000 -0.675 -0.599
ar.L13 -0.5921 0.019 -30.791 0.000 -0.630 -0.554
ar.L14 -0.5979 0.019 -30.773 0.000 -0.636 -0.560
ar.L15 -0.5363 0.019 -27.536 0.000 -0.575 -0.498
ar.L16 -0.5274 0.019 -27.220 0.000 -0.565 -0.489
ar.L17 -0.4306 0.019 -22.472 0.000 -0.468 -0.393
ar.L18 -0.4427 0.019 -23.587 0.000 -0.480 -0.406
ar.L19 -0.3638 0.018 -19.793 0.000 -0.400 -0.328
ar.L20 -0.4008 0.018 -22.584 0.000 -0.436 -0.366
ar.L21 -0.3816 0.017 -22.460 0.000 -0.415 -0.348
ar.L22 -0.4229 0.017 -25.578 0.000 -0.455 -0.390
ar.L23 -0.3234 0.016 -20.417 0.000 -0.354 -0.292
ar.L24 -0.3493 0.015 -23.904 0.000 -0.378 -0.321
ar.L25 -0.2654 0.013 -19.670 0.000 -0.292 -0.239
ar.L26 -0.2706 0.012 -22.470 0.000 -0.294 -0.247
ar.L27 -0.1783 0.011 -16.738 0.000 -0.199 -0.157
ar.L28 -0.1926 0.009 -21.358 0.000 -0.210 -0.175
ar.L29 -0.0921 0.007 -12.315 0.000 -0.107 -0.077
ar.L30 -0.0913 0.005 -18.027 0.000 -0.101 -0.081
ma.L1 -0.9444 0.003 -306.574 0.000 -0.950 -0.938
sigma2 0.0053 1.73e-05 305.221 0.000 0.005 0.005
===================================================================================
Ljung-Box (L1) (Q): 7.81 Jarque-Bera (JB): 591350.37
Prob(Q): 0.01 Prob(JB): 0.00
Heteroskedasticity (H): 19.07 Skew: 0.64
Prob(H) (two-sided): 0.00 Kurtosis: 31.14
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891 19.941735
17892 19.976421
17893 20.026530
17894 20.033310
17895 20.072177
17896 20.077084
17897 20.130767
17898 20.126092
17899 20.191097
17900 20.178801
Name: predicted_mean, dtype: float64
lower price upper price
17891 19.799282 20.084188
17892 19.772369 20.180473
17893 19.764723 20.288337
17894 19.721516 20.345104
17895 19.708484 20.435871
17896 19.665183 20.488985
17897 19.666004 20.595531
17898 19.611462 20.640722
17899 19.621823 20.760371
17900 19.555704 20.801899
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.234597404703211 Weighted Mean Absolute Percentage Error (WMAPE): 1.1579513037803237
forecastplusyahoo('SAHOL', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 12.4338 SAHOL
2021-12-27 10:00:00+03:00 12.5345 SAHOL
2021-12-27 11:00:00+03:00 12.4978 SAHOL
2021-12-27 12:00:00+03:00 12.4978 SAHOL
2021-12-27 13:00:00+03:00 12.5985 SAHOL
... ... ...
2023-09-22 14:00:00+03:00 56.5000 SAHOL
2023-09-22 15:00:00+03:00 56.5000 SAHOL
2023-09-22 16:00:00+03:00 56.7000 SAHOL
2023-09-22 17:00:00+03:00 56.4000 SAHOL
2023-09-22 18:00:00+03:00 56.5000 SAHOL
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for SAHOL.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 12.4338
2021-12-27 06:30:00+00:00 13.6500
2021-12-27 07:00:00+00:00 12.5345
2021-12-27 07:30:00+00:00 13.7000
2021-12-27 08:00:00+00:00 12.4978
2021-12-27 08:30:00+00:00 13.6900
2021-12-27 09:00:00+00:00 12.4978
2021-12-27 09:30:00+00:00 13.6700
2021-12-27 10:00:00+00:00 12.5985
2021-12-27 10:30:00+00:00 13.6800
2021-12-27 11:00:00+00:00 12.4155
2021-12-27 11:30:00+00:00 13.4600
2021-12-27 12:00:00+00:00 12.3606
2021-12-27 12:30:00+00:00 13.4200
2021-12-27 13:00:00+00:00 12.3240
2021-12-27 13:30:00+00:00 13.5100
2021-12-27 14:00:00+00:00 12.2416
2021-12-27 15:00:00+00:00 12.2325
2021-12-28 06:00:00+00:00 12.3514
2021-12-28 06:30:00+00:00 13.4200
2021-12-28 07:00:00+00:00 12.3148
2021-12-28 07:30:00+00:00 13.4400
2021-12-28 08:00:00+00:00 12.2050
2021-12-28 08:30:00+00:00 13.2700
2021-12-28 09:00:00+00:00 12.1410
2021-12-28 09:30:00+00:00 13.2400
2021-12-28 10:00:00+00:00 12.0587
2021-12-28 10:30:00+00:00 13.1300
2021-12-28 11:00:00+00:00 11.9489
2021-12-28 11:30:00+00:00 13.1500
2021-12-28 12:00:00+00:00 12.0495
2021-12-28 12:30:00+00:00 13.1900
2021-12-28 13:00:00+00:00 11.9763
2021-12-28 13:30:00+00:00 12.9200
2021-12-28 14:00:00+00:00 11.9214
2021-12-28 15:00:00+00:00 11.9123
2021-12-29 06:00:00+00:00 11.8574
2021-12-29 06:30:00+00:00 12.8700
2021-12-29 07:00:00+00:00 11.7934
2021-12-29 07:30:00+00:00 13.0400
2021-12-29 08:00:00+00:00 11.9672
2021-12-29 08:30:00+00:00 13.0600
2021-12-29 09:00:00+00:00 12.0038
2021-12-29 09:30:00+00:00 13.1300
2021-12-29 10:00:00+00:00 12.0403
2021-12-29 10:30:00+00:00 13.0800
2021-12-29 11:00:00+00:00 12.0221
2021-12-29 11:30:00+00:00 13.2100
2021-12-29 12:00:00+00:00 12.1135
2021-12-29 12:30:00+00:00 13.4000
price
2023-09-21 10:00:00+00:00 55.750000
2023-09-21 10:30:00+00:00 55.900002
2023-09-21 11:00:00+00:00 56.450000
2023-09-21 11:30:00+00:00 56.299999
2023-09-21 12:00:00+00:00 56.450000
2023-09-21 12:30:00+00:00 56.900002
2023-09-21 13:00:00+00:00 56.600000
2023-09-21 13:30:00+00:00 56.799999
2023-09-21 14:00:00+00:00 56.900000
2023-09-21 14:30:00+00:00 56.900002
2023-09-21 15:00:00+00:00 56.950000
2023-09-22 06:00:00+00:00 57.400000
2023-09-22 06:30:00+00:00 57.299999
2023-09-22 07:00:00+00:00 57.050000
2023-09-22 07:30:00+00:00 57.000000
2023-09-22 08:00:00+00:00 56.850000
2023-09-22 08:30:00+00:00 56.900002
2023-09-22 09:00:00+00:00 56.950000
2023-09-22 09:30:00+00:00 56.849998
2023-09-22 10:00:00+00:00 56.950000
2023-09-22 10:30:00+00:00 56.650002
2023-09-22 11:00:00+00:00 56.500000
2023-09-22 11:30:00+00:00 56.299999
2023-09-22 12:00:00+00:00 56.500000
2023-09-22 12:30:00+00:00 56.799999
2023-09-22 13:00:00+00:00 56.700000
2023-09-22 13:30:00+00:00 56.299999
2023-09-22 14:00:00+00:00 56.400000
2023-09-22 14:30:00+00:00 56.400002
2023-09-22 15:00:00+00:00 56.500000
Mean of the first 10 values: price 57.66
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 56.75
2023-09-25 10:00:00+03:00 57.40
2023-09-25 11:00:00+03:00 57.45
2023-09-25 12:00:00+03:00 57.75
2023-09-25 13:00:00+03:00 57.90
2023-09-25 14:00:00+03:00 57.75
2023-09-25 15:00:00+03:00 57.60
2023-09-25 16:00:00+03:00 57.95
2023-09-25 17:00:00+03:00 57.95
2023-09-25 18:00:00+03:00 58.10
price
2018-01-02 10:00:00+03:00 0.000000
2018-01-02 11:00:00+03:00 0.063000
2018-01-02 12:00:00+03:00 -0.031500
2018-01-02 13:00:00+03:00 0.023600
2018-01-02 14:00:00+03:00 0.015700
... ...
2023-09-22 13:00:00+00:00 -0.099999
2023-09-22 13:30:00+00:00 -0.400001
2023-09-22 14:00:00+00:00 0.100001
2023-09-22 14:30:00+00:00 0.000002
2023-09-22 15:00:00+00:00 0.099998
[17889 rows x 1 columns]
ADF Statistic: -21.755710529704338
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 3, 1) Log Likelihood -2969.986
Date: Sun, 24 Dec 2023 AIC 6003.972
Time: 22:16:28 BIC 6253.310
Sample: 0 HQIC 6085.991
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.6593 0.004 -378.645 0.000 -1.668 -1.651
ar.L2 -1.6006 0.010 -165.187 0.000 -1.620 -1.582
ar.L3 -1.6381 0.014 -113.783 0.000 -1.666 -1.610
ar.L4 -1.5950 0.018 -86.726 0.000 -1.631 -1.559
ar.L5 -1.5562 0.022 -70.010 0.000 -1.600 -1.513
ar.L6 -1.4618 0.026 -57.182 0.000 -1.512 -1.412
ar.L7 -1.4311 0.028 -51.153 0.000 -1.486 -1.376
ar.L8 -1.3870 0.030 -46.442 0.000 -1.445 -1.328
ar.L9 -1.3640 0.032 -43.143 0.000 -1.426 -1.302
ar.L10 -1.2635 0.033 -37.988 0.000 -1.329 -1.198
ar.L11 -1.1918 0.035 -34.423 0.000 -1.260 -1.124
ar.L12 -1.1463 0.036 -32.252 0.000 -1.216 -1.077
ar.L13 -1.0614 0.036 -29.588 0.000 -1.132 -0.991
ar.L14 -1.0159 0.036 -28.424 0.000 -1.086 -0.946
ar.L15 -0.9727 0.036 -27.303 0.000 -1.043 -0.903
ar.L16 -1.0455 0.035 -29.538 0.000 -1.115 -0.976
ar.L17 -1.0822 0.035 -30.604 0.000 -1.152 -1.013
ar.L18 -0.6771 0.036 -18.951 0.000 -0.747 -0.607
ar.L19 -0.2892 0.035 -8.262 0.000 -0.358 -0.221
ar.L20 -0.4053 0.033 -12.117 0.000 -0.471 -0.340
ar.L21 -0.4374 0.032 -13.629 0.000 -0.500 -0.375
ar.L22 -0.4050 0.031 -13.242 0.000 -0.465 -0.345
ar.L23 -0.4031 0.029 -14.001 0.000 -0.460 -0.347
ar.L24 -0.3492 0.027 -13.102 0.000 -0.401 -0.297
ar.L25 -0.3026 0.024 -12.362 0.000 -0.351 -0.255
ar.L26 -0.1883 0.022 -8.677 0.000 -0.231 -0.146
ar.L27 -0.1244 0.018 -6.739 0.000 -0.161 -0.088
ar.L28 -0.0703 0.015 -4.755 0.000 -0.099 -0.041
ar.L29 -0.0556 0.011 -5.011 0.000 -0.077 -0.034
ar.L30 0.0289 0.006 5.137 0.000 0.018 0.040
ma.L1 -0.9547 0.003 -307.651 0.000 -0.961 -0.949
sigma2 0.0809 0.000 225.706 0.000 0.080 0.082
===================================================================================
Ljung-Box (L1) (Q): 0.03 Jarque-Bera (JB): 152125.23
Prob(Q): 0.86 Prob(JB): 0.00
Heteroskedasticity (H): 48.87 Skew: -0.03
Prob(H) (two-sided): 0.00 Kurtosis: 17.29
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 56.569994
17891 56.335954
17892 56.338118
17893 56.229987
17894 56.291149
17895 56.309717
17896 56.225383
17897 56.301704
17898 56.166691
17899 55.986905
Name: predicted_mean, dtype: float64
lower price upper price
17890 56.012651 57.127337
17891 55.738531 56.933378
17892 55.559926 57.116310
17893 55.397390 57.062585
17894 55.315654 57.266645
17895 55.263555 57.355880
17896 55.038439 57.412326
17897 55.036884 57.566524
17898 54.766125 57.567256
17899 54.501757 57.472054
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 1.470576171892774 Weighted Mean Absolute Percentage Error (WMAPE): 2.401040004672276
forecastplusyahoo('SASA', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 10.5956 SASA
2021-12-27 10:00:00+03:00 11.0217 SASA
2021-12-27 11:00:00+03:00 11.0108 SASA
2021-12-27 12:00:00+03:00 11.0326 SASA
2021-12-27 13:00:00+03:00 11.0108 SASA
... ... ...
2023-09-22 14:00:00+03:00 45.6800 SASA
2023-09-22 15:00:00+03:00 45.5400 SASA
2023-09-22 16:00:00+03:00 45.5600 SASA
2023-09-22 17:00:00+03:00 45.5200 SASA
2023-09-22 18:00:00+03:00 45.5200 SASA
[4322 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for SASA.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 10.595600
2021-12-27 06:30:00+00:00 10.840745
2021-12-27 07:00:00+00:00 11.021700
2021-12-27 07:30:00+00:00 10.733411
2021-12-27 08:00:00+00:00 11.010800
2021-12-27 08:30:00+00:00 10.915878
2021-12-27 09:00:00+00:00 11.032600
2021-12-27 09:30:00+00:00 10.787078
2021-12-27 10:00:00+00:00 11.010800
2021-12-27 10:30:00+00:00 10.851479
2021-12-27 11:00:00+00:00 10.934800
2021-12-27 11:30:00+00:00 10.711944
2021-12-27 12:00:00+00:00 10.869600
2021-12-27 12:30:00+00:00 10.703357
2021-12-27 13:00:00+00:00 11.000000
2021-12-27 13:30:00+00:00 11.044680
2021-12-27 14:00:00+00:00 11.119600
2021-12-27 15:00:00+00:00 11.163000
2021-12-28 06:00:00+00:00 11.391300
2021-12-28 06:30:00+00:00 11.270082
2021-12-28 07:00:00+00:00 11.423900
2021-12-28 07:30:00+00:00 11.152014
2021-12-28 08:00:00+00:00 11.260800
2021-12-28 08:30:00+00:00 11.141280
2021-12-28 09:00:00+00:00 11.228300
2021-12-28 09:30:00+00:00 11.152014
2021-12-28 10:00:00+00:00 11.195700
2021-12-28 10:30:00+00:00 11.055413
2021-12-28 11:00:00+00:00 11.173900
2021-12-28 11:30:00+00:00 11.066146
2021-12-28 12:00:00+00:00 11.184800
2021-12-28 12:30:00+00:00 11.044680
2021-12-28 13:00:00+00:00 11.217300
2021-12-28 13:30:00+00:00 10.905145
2021-12-28 14:00:00+00:00 11.402200
2021-12-28 15:00:00+00:00 11.478300
2021-12-29 06:00:00+00:00 11.576100
2021-12-29 06:30:00+00:00 11.270082
2021-12-29 07:00:00+00:00 11.445700
2021-12-29 07:30:00+00:00 11.420349
2021-12-29 08:00:00+00:00 11.500000
2021-12-29 08:30:00+00:00 11.323749
2021-12-29 09:00:00+00:00 11.500000
2021-12-29 09:30:00+00:00 11.323749
2021-12-29 10:00:00+00:00 11.489100
2021-12-29 10:30:00+00:00 11.355948
2021-12-29 11:00:00+00:00 11.543500
2021-12-29 11:30:00+00:00 11.355948
2021-12-29 12:00:00+00:00 11.478300
2021-12-29 12:30:00+00:00 11.377416
price
2023-09-21 10:00:00+00:00 44.520000
2023-09-21 10:30:00+00:00 45.020000
2023-09-21 11:00:00+00:00 45.640000
2023-09-21 11:30:00+00:00 45.500000
2023-09-21 12:00:00+00:00 45.520000
2023-09-21 12:30:00+00:00 45.919998
2023-09-21 13:00:00+00:00 45.780000
2023-09-21 13:30:00+00:00 46.000000
2023-09-21 14:00:00+00:00 46.260000
2023-09-21 14:30:00+00:00 46.259998
2023-09-21 15:00:00+00:00 46.300000
2023-09-22 06:00:00+00:00 46.300000
2023-09-22 06:30:00+00:00 46.419998
2023-09-22 07:00:00+00:00 46.340000
2023-09-22 07:30:00+00:00 46.320000
2023-09-22 08:00:00+00:00 46.440000
2023-09-22 08:30:00+00:00 46.320000
2023-09-22 09:00:00+00:00 46.200000
2023-09-22 09:30:00+00:00 46.160000
2023-09-22 10:00:00+00:00 45.920000
2023-09-22 10:30:00+00:00 45.700001
2023-09-22 11:00:00+00:00 45.680000
2023-09-22 11:30:00+00:00 45.500000
2023-09-22 12:00:00+00:00 45.540000
2023-09-22 12:30:00+00:00 45.639999
2023-09-22 13:00:00+00:00 45.560000
2023-09-22 13:30:00+00:00 45.380001
2023-09-22 14:00:00+00:00 45.520000
2023-09-22 14:30:00+00:00 45.520000
2023-09-22 15:00:00+00:00 45.520000
Mean of the first 10 values: price 45.648
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 45.60
2023-09-25 10:00:00+03:00 45.28
2023-09-25 11:00:00+03:00 45.54
2023-09-25 12:00:00+03:00 45.66
2023-09-25 13:00:00+03:00 45.54
2023-09-25 14:00:00+03:00 45.46
2023-09-25 15:00:00+03:00 45.66
2023-09-25 16:00:00+03:00 45.92
2023-09-25 17:00:00+03:00 45.92
2023-09-25 18:00:00+03:00 45.90
price
2018-01-02 10:00:00+03:00 6.900000e-03
2018-01-02 11:00:00+03:00 2.770000e-02
2018-01-02 12:00:00+03:00 1.930000e-02
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 0.000000e+00
... ...
2023-09-22 13:00:00+00:00 -7.999939e-02
2023-09-22 13:30:00+00:00 -1.799989e-01
2023-09-22 14:00:00+00:00 1.399989e-01
2023-09-22 14:30:00+00:00 4.577637e-07
2023-09-22 15:00:00+00:00 -4.577637e-07
[17887 rows x 1 columns]
ADF Statistic: -20.598576779626608
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17888
Model: ARIMA(30, 1, 1) Log Likelihood -4762.682
Date: Sun, 24 Dec 2023 AIC 9589.363
Time: 22:04:22 BIC 9838.702
Sample: 0 HQIC 9671.382
- 17888
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.1017 0.004 -261.275 0.000 -1.110 -1.093
ar.L2 -0.1216 0.005 -25.825 0.000 -0.131 -0.112
ar.L3 -0.0341 0.005 -6.279 0.000 -0.045 -0.023
ar.L4 -0.0398 0.006 -7.153 0.000 -0.051 -0.029
ar.L5 -0.0207 0.005 -4.379 0.000 -0.030 -0.011
ar.L6 -0.0286 0.005 -5.902 0.000 -0.038 -0.019
ar.L7 -0.0129 0.005 -2.369 0.018 -0.024 -0.002
ar.L8 0.0025 0.007 0.370 0.712 -0.011 0.016
ar.L9 0.0386 0.007 5.495 0.000 0.025 0.052
ar.L10 0.0497 0.007 7.501 0.000 0.037 0.063
ar.L11 0.0325 0.006 5.040 0.000 0.020 0.045
ar.L12 -0.0015 0.006 -0.252 0.801 -0.013 0.010
ar.L13 0.0126 0.005 2.303 0.021 0.002 0.023
ar.L14 -0.0046 0.005 -0.853 0.394 -0.015 0.006
ar.L15 0.0202 0.006 3.503 0.000 0.009 0.031
ar.L16 0.0586 0.006 10.492 0.000 0.048 0.070
ar.L17 0.1060 0.004 29.715 0.000 0.099 0.113
ar.L18 0.2468 0.002 103.409 0.000 0.242 0.251
ar.L19 0.1875 0.002 78.199 0.000 0.183 0.192
ar.L20 -0.0681 0.004 -19.044 0.000 -0.075 -0.061
ar.L21 -0.0510 0.005 -10.439 0.000 -0.061 -0.041
ar.L22 -0.0312 0.004 -7.038 0.000 -0.040 -0.023
ar.L23 -0.0635 0.005 -11.993 0.000 -0.074 -0.053
ar.L24 -0.0284 0.005 -5.319 0.000 -0.039 -0.018
ar.L25 -0.0519 0.005 -10.174 0.000 -0.062 -0.042
ar.L26 -0.0010 0.006 -0.157 0.875 -0.013 0.011
ar.L27 -0.0068 0.006 -1.221 0.222 -0.018 0.004
ar.L28 -0.0330 0.006 -5.195 0.000 -0.045 -0.021
ar.L29 -0.0171 0.006 -2.840 0.005 -0.029 -0.005
ar.L30 0.0317 0.004 8.802 0.000 0.025 0.039
ma.L1 0.9059 0.003 272.405 0.000 0.899 0.912
sigma2 0.0997 0.000 492.330 0.000 0.099 0.100
===================================================================================
Ljung-Box (L1) (Q): 0.04 Jarque-Bera (JB): 15877988.14
Prob(Q): 0.83 Prob(JB): 0.00
Heteroskedasticity (H): 1721.76 Skew: -0.99
Prob(H) (two-sided): 0.00 Kurtosis: 148.95
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17888 45.472006
17889 45.506526
17890 45.441232
17891 45.478445
17892 45.418288
17893 45.389174
17894 45.342015
17895 45.316609
17896 45.262937
17897 45.290115
Name: predicted_mean, dtype: float64
lower price upper price
17888 44.853236 46.090775
17889 44.712473 46.300578
17890 44.471954 46.410509
17891 44.394431 46.562460
17892 44.209232 46.627344
17893 44.088745 46.689602
17894 43.941464 46.742567
17895 43.837090 46.796127
17896 43.693951 46.831923
17897 43.642724 46.937507
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.37404949455119474 Weighted Mean Absolute Percentage Error (WMAPE): 0.6144695962537068
forecastplusyahoo('SISE', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 13.3510 SISE
2021-12-27 10:00:00+03:00 13.5826 SISE
2021-12-27 11:00:00+03:00 13.4379 SISE
2021-12-27 12:00:00+03:00 13.4282 SISE
2021-12-27 13:00:00+03:00 13.4186 SISE
... ... ...
2023-09-22 14:00:00+03:00 52.9000 SISE
2023-09-22 15:00:00+03:00 52.7500 SISE
2023-09-22 16:00:00+03:00 53.3000 SISE
2023-09-22 17:00:00+03:00 54.7000 SISE
2023-09-22 18:00:00+03:00 54.7000 SISE
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for SISE.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 13.3510
2021-12-27 06:30:00+00:00 14.0000
2021-12-27 07:00:00+00:00 13.5826
2021-12-27 07:30:00+00:00 13.9200
2021-12-27 08:00:00+00:00 13.4379
2021-12-27 08:30:00+00:00 13.9600
2021-12-27 09:00:00+00:00 13.4282
2021-12-27 09:30:00+00:00 13.8500
2021-12-27 10:00:00+00:00 13.4186
2021-12-27 10:30:00+00:00 13.8800
2021-12-27 11:00:00+00:00 13.3028
2021-12-27 11:30:00+00:00 13.6400
2021-12-27 12:00:00+00:00 13.1870
2021-12-27 12:30:00+00:00 13.6400
2021-12-27 13:00:00+00:00 13.2352
2021-12-27 13:30:00+00:00 13.7800
2021-12-27 14:00:00+00:00 13.1099
2021-12-27 15:00:00+00:00 13.0712
2021-12-28 06:00:00+00:00 13.3221
2021-12-28 06:30:00+00:00 13.7800
2021-12-28 07:00:00+00:00 13.4861
2021-12-28 07:30:00+00:00 13.8700
2021-12-28 08:00:00+00:00 13.4282
2021-12-28 08:30:00+00:00 13.9200
2021-12-28 09:00:00+00:00 13.3896
2021-12-28 09:30:00+00:00 13.8700
2021-12-28 10:00:00+00:00 13.3606
2021-12-28 10:30:00+00:00 13.7900
2021-12-28 11:00:00+00:00 13.1870
2021-12-28 11:30:00+00:00 13.7800
2021-12-28 12:00:00+00:00 13.2739
2021-12-28 12:30:00+00:00 13.7500
2021-12-28 13:00:00+00:00 13.1581
2021-12-28 13:30:00+00:00 13.3300
2021-12-28 14:00:00+00:00 12.9941
2021-12-28 15:00:00+00:00 12.9363
2021-12-29 06:00:00+00:00 12.9265
2021-12-29 06:30:00+00:00 13.4000
2021-12-29 07:00:00+00:00 12.9169
2021-12-29 07:30:00+00:00 13.5300
2021-12-29 08:00:00+00:00 13.1677
2021-12-29 08:30:00+00:00 13.6000
2021-12-29 09:00:00+00:00 13.2256
2021-12-29 09:30:00+00:00 13.6200
2021-12-29 10:00:00+00:00 13.2063
2021-12-29 10:30:00+00:00 13.6300
2021-12-29 11:00:00+00:00 13.2063
2021-12-29 11:30:00+00:00 13.7800
2021-12-29 12:00:00+00:00 13.3124
2021-12-29 12:30:00+00:00 13.8600
price
2023-09-21 10:00:00+00:00 51.250000
2023-09-21 10:30:00+00:00 51.900002
2023-09-21 11:00:00+00:00 52.400000
2023-09-21 11:30:00+00:00 52.500000
2023-09-21 12:00:00+00:00 52.200000
2023-09-21 12:30:00+00:00 52.349998
2023-09-21 13:00:00+00:00 52.450000
2023-09-21 13:30:00+00:00 52.750000
2023-09-21 14:00:00+00:00 53.150000
2023-09-21 14:30:00+00:00 53.150002
2023-09-21 15:00:00+00:00 52.950000
2023-09-22 06:00:00+00:00 53.750000
2023-09-22 06:30:00+00:00 53.750000
2023-09-22 07:00:00+00:00 53.650000
2023-09-22 07:30:00+00:00 53.500000
2023-09-22 08:00:00+00:00 53.550000
2023-09-22 08:30:00+00:00 53.400002
2023-09-22 09:00:00+00:00 53.550000
2023-09-22 09:30:00+00:00 53.500000
2023-09-22 10:00:00+00:00 53.500000
2023-09-22 10:30:00+00:00 53.299999
2023-09-22 11:00:00+00:00 52.900000
2023-09-22 11:30:00+00:00 52.700001
2023-09-22 12:00:00+00:00 52.750000
2023-09-22 12:30:00+00:00 53.200001
2023-09-22 13:00:00+00:00 53.300000
2023-09-22 13:30:00+00:00 53.099998
2023-09-22 14:00:00+00:00 54.700000
2023-09-22 14:30:00+00:00 54.700001
2023-09-22 15:00:00+00:00 54.700000
Mean of the first 10 values: price 55.305
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 54.80
2023-09-25 10:00:00+03:00 55.40
2023-09-25 11:00:00+03:00 55.55
2023-09-25 12:00:00+03:00 55.40
2023-09-25 13:00:00+03:00 55.45
2023-09-25 14:00:00+03:00 55.15
2023-09-25 15:00:00+03:00 55.30
2023-09-25 16:00:00+03:00 55.55
2023-09-25 17:00:00+03:00 55.25
2023-09-25 18:00:00+03:00 55.20
price
2018-01-02 10:00:00+03:00 -5.130000e-02
2018-01-02 11:00:00+03:00 2.560000e-02
2018-01-02 12:00:00+03:00 0.000000e+00
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 2.570000e-02
... ...
2023-09-22 13:00:00+00:00 9.999924e-02
2023-09-22 13:30:00+00:00 -2.000015e-01
2023-09-22 14:00:00+00:00 1.600002e+00
2023-09-22 14:30:00+00:00 7.629395e-07
2023-09-22 15:00:00+00:00 -7.629395e-07
[17889 rows x 1 columns]
ADF Statistic: -21.263395625979722
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 1, 1) Log Likelihood 3464.199
Date: Sun, 24 Dec 2023 AIC -6864.398
Time: 22:07:13 BIC -6615.056
Sample: 0 HQIC -6782.378
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.1915 0.005 -251.046 0.000 -1.201 -1.182
ar.L2 -0.1777 0.004 -40.023 0.000 -0.186 -0.169
ar.L3 0.0014 0.006 0.246 0.806 -0.010 0.012
ar.L4 -0.0511 0.006 -8.036 0.000 -0.064 -0.039
ar.L5 -0.0009 0.006 -0.156 0.876 -0.013 0.011
ar.L6 -0.0113 0.006 -1.854 0.064 -0.023 0.001
ar.L7 -0.0214 0.006 -3.575 0.000 -0.033 -0.010
ar.L8 -0.0170 0.006 -2.680 0.007 -0.029 -0.005
ar.L9 0.0097 0.006 1.528 0.127 -0.003 0.022
ar.L10 0.0487 0.007 7.008 0.000 0.035 0.062
ar.L11 0.0482 0.007 6.993 0.000 0.035 0.062
ar.L12 0.0209 0.007 2.969 0.003 0.007 0.035
ar.L13 0.0338 0.007 4.894 0.000 0.020 0.047
ar.L14 0.0251 0.007 3.787 0.000 0.012 0.038
ar.L15 0.0120 0.007 1.798 0.072 -0.001 0.025
ar.L16 -0.0520 0.006 -8.304 0.000 -0.064 -0.040
ar.L17 -0.0526 0.006 -9.031 0.000 -0.064 -0.041
ar.L18 0.1165 0.005 22.669 0.000 0.106 0.127
ar.L19 0.2086 0.004 46.383 0.000 0.200 0.217
ar.L20 -0.0780 0.005 -14.401 0.000 -0.089 -0.067
ar.L21 -0.1929 0.006 -34.172 0.000 -0.204 -0.182
ar.L22 -0.0668 0.007 -9.565 0.000 -0.081 -0.053
ar.L23 -0.0542 0.006 -8.348 0.000 -0.067 -0.041
ar.L24 -0.0163 0.006 -2.583 0.010 -0.029 -0.004
ar.L25 0.0088 0.006 1.425 0.154 -0.003 0.021
ar.L26 0.0298 0.007 4.457 0.000 0.017 0.043
ar.L27 0.0037 0.007 0.507 0.612 -0.011 0.018
ar.L28 -0.0293 0.008 -3.839 0.000 -0.044 -0.014
ar.L29 -0.0043 0.007 -0.597 0.551 -0.018 0.010
ar.L30 0.0590 0.005 11.441 0.000 0.049 0.069
ma.L1 0.8745 0.004 227.320 0.000 0.867 0.882
sigma2 0.0397 0.000 289.283 0.000 0.039 0.040
===================================================================================
Ljung-Box (L1) (Q): 1.43 Jarque-Bera (JB): 547526.66
Prob(Q): 0.23 Prob(JB): 0.00
Heteroskedasticity (H): 69.39 Skew: 0.81
Prob(H) (two-sided): 0.00 Kurtosis: 30.05
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 54.650096
17891 54.566456
17892 54.497238
17893 54.515978
17894 54.450404
17895 54.515582
17896 54.512946
17897 54.673612
17898 54.602040
17899 54.591857
Name: predicted_mean, dtype: float64
lower price upper price
17890 54.259510 55.040681
17891 54.093465 55.039448
17892 53.911846 55.082631
17893 53.869492 55.162464
17894 53.727082 55.173726
17895 53.738742 55.292422
17896 53.674661 55.351230
17897 53.789416 55.557807
17898 53.665582 55.538499
17899 53.611789 55.571925
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.7865079316180594 Weighted Mean Absolute Percentage Error (WMAPE): 1.3513770747596507
forecastplusyahoo('TAVHL', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 33.24 TAVHL
2021-12-27 10:00:00+03:00 33.68 TAVHL
2021-12-27 11:00:00+03:00 33.36 TAVHL
2021-12-27 12:00:00+03:00 33.20 TAVHL
2021-12-27 13:00:00+03:00 33.20 TAVHL
... ... ...
2023-09-22 14:00:00+03:00 118.70 TAVHL
2023-09-22 15:00:00+03:00 118.70 TAVHL
2023-09-22 16:00:00+03:00 118.80 TAVHL
2023-09-22 17:00:00+03:00 119.20 TAVHL
2023-09-22 18:00:00+03:00 119.20 TAVHL
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for TAVHL.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 33.240000
2021-12-27 06:30:00+00:00 33.220001
2021-12-27 07:00:00+00:00 33.680000
2021-12-27 07:30:00+00:00 33.340000
2021-12-27 08:00:00+00:00 33.360000
2021-12-27 08:30:00+00:00 33.360001
2021-12-27 09:00:00+00:00 33.200000
2021-12-27 09:30:00+00:00 33.020000
2021-12-27 10:00:00+00:00 33.200000
2021-12-27 10:30:00+00:00 33.099998
2021-12-27 11:00:00+00:00 32.640000
2021-12-27 11:30:00+00:00 32.560001
2021-12-27 12:00:00+00:00 32.760000
2021-12-27 12:30:00+00:00 32.759998
2021-12-27 13:00:00+00:00 32.780000
2021-12-27 13:30:00+00:00 33.000000
2021-12-27 14:00:00+00:00 32.600000
2021-12-27 15:00:00+00:00 32.500000
2021-12-28 06:00:00+00:00 32.600000
2021-12-28 06:30:00+00:00 32.680000
2021-12-28 07:00:00+00:00 32.900000
2021-12-28 07:30:00+00:00 32.900002
2021-12-28 08:00:00+00:00 32.700000
2021-12-28 08:30:00+00:00 32.660000
2021-12-28 09:00:00+00:00 32.560000
2021-12-28 09:30:00+00:00 32.580002
2021-12-28 10:00:00+00:00 32.420000
2021-12-28 10:30:00+00:00 32.259998
2021-12-28 11:00:00+00:00 32.180000
2021-12-28 11:30:00+00:00 32.459999
2021-12-28 12:00:00+00:00 32.300000
2021-12-28 12:30:00+00:00 32.119999
2021-12-28 13:00:00+00:00 31.500000
2021-12-28 13:30:00+00:00 31.100000
2021-12-28 14:00:00+00:00 31.540000
2021-12-28 15:00:00+00:00 31.560000
2021-12-29 06:00:00+00:00 31.120000
2021-12-29 06:30:00+00:00 30.959999
2021-12-29 07:00:00+00:00 31.220000
2021-12-29 07:30:00+00:00 31.360001
2021-12-29 08:00:00+00:00 31.560000
2021-12-29 08:30:00+00:00 31.459999
2021-12-29 09:00:00+00:00 31.780000
2021-12-29 09:30:00+00:00 31.719999
2021-12-29 10:00:00+00:00 31.780000
2021-12-29 10:30:00+00:00 31.639999
2021-12-29 11:00:00+00:00 32.140000
2021-12-29 11:30:00+00:00 32.299999
2021-12-29 12:00:00+00:00 32.500000
2021-12-29 12:30:00+00:00 32.560001
price
2023-09-21 10:00:00+00:00 117.700000
2023-09-21 10:30:00+00:00 118.900002
2023-09-21 11:00:00+00:00 121.900000
2023-09-21 11:30:00+00:00 121.000000
2023-09-21 12:00:00+00:00 120.600000
2023-09-21 12:30:00+00:00 121.400002
2023-09-21 13:00:00+00:00 120.700000
2023-09-21 13:30:00+00:00 121.199997
2023-09-21 14:00:00+00:00 121.900000
2023-09-21 14:30:00+00:00 121.900002
2023-09-21 15:00:00+00:00 122.900000
2023-09-22 06:00:00+00:00 119.500000
2023-09-22 06:30:00+00:00 119.900002
2023-09-22 07:00:00+00:00 119.000000
2023-09-22 07:30:00+00:00 119.599998
2023-09-22 08:00:00+00:00 119.300000
2023-09-22 08:30:00+00:00 118.900002
2023-09-22 09:00:00+00:00 119.200000
2023-09-22 09:30:00+00:00 119.400002
2023-09-22 10:00:00+00:00 119.400000
2023-09-22 10:30:00+00:00 119.199997
2023-09-22 11:00:00+00:00 118.700000
2023-09-22 11:30:00+00:00 118.199997
2023-09-22 12:00:00+00:00 118.700000
2023-09-22 12:30:00+00:00 119.099998
2023-09-22 13:00:00+00:00 118.800000
2023-09-22 13:30:00+00:00 118.699997
2023-09-22 14:00:00+00:00 119.200000
2023-09-22 14:30:00+00:00 119.199997
2023-09-22 15:00:00+00:00 119.200000
Mean of the first 10 values: price 121.53
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 119.3
2023-09-25 10:00:00+03:00 118.9
2023-09-25 11:00:00+03:00 121.1
2023-09-25 12:00:00+03:00 121.1
2023-09-25 13:00:00+03:00 122.7
2023-09-25 14:00:00+03:00 121.9
2023-09-25 15:00:00+03:00 121.8
2023-09-25 16:00:00+03:00 121.9
2023-09-25 17:00:00+03:00 123.5
2023-09-25 18:00:00+03:00 123.1
price
2018-01-02 10:00:00+03:00 0.163500
2018-01-02 11:00:00+03:00 -0.065400
2018-01-02 12:00:00+03:00 0.049000
2018-01-02 13:00:00+03:00 0.000000
2018-01-02 14:00:00+03:00 -0.016400
... ...
2023-09-22 13:00:00+00:00 -0.299998
2023-09-22 13:30:00+00:00 -0.100003
2023-09-22 14:00:00+00:00 0.500003
2023-09-22 14:30:00+00:00 -0.000003
2023-09-22 15:00:00+00:00 0.000003
[17889 rows x 1 columns]
ADF Statistic: -28.682964137099823
p-value: 0.0
Critical Values: {'1%': -3.4307160308760696, '5%': -2.8617017717195736, '10%': -2.566856106850706}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 3, 1) Log Likelihood -8407.569
Date: Sun, 24 Dec 2023 AIC 16879.137
Time: 22:20:36 BIC 17128.476
Sample: 0 HQIC 16961.157
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.9868 0.003 -305.198 0.000 -0.993 -0.980
ar.L2 -0.9750 0.005 -191.263 0.000 -0.985 -0.965
ar.L3 -0.9487 0.007 -131.083 0.000 -0.963 -0.935
ar.L4 -0.9108 0.008 -108.663 0.000 -0.927 -0.894
ar.L5 -0.8752 0.010 -90.847 0.000 -0.894 -0.856
ar.L6 -0.8296 0.011 -77.418 0.000 -0.851 -0.809
ar.L7 -0.7967 0.012 -67.791 0.000 -0.820 -0.774
ar.L8 -0.7680 0.012 -61.513 0.000 -0.793 -0.744
ar.L9 -0.7328 0.013 -56.767 0.000 -0.758 -0.707
ar.L10 -0.6941 0.014 -51.380 0.000 -0.721 -0.668
ar.L11 -0.6454 0.014 -45.496 0.000 -0.673 -0.618
ar.L12 -0.6300 0.014 -43.845 0.000 -0.658 -0.602
ar.L13 -0.5922 0.015 -40.208 0.000 -0.621 -0.563
ar.L14 -0.5422 0.015 -36.538 0.000 -0.571 -0.513
ar.L15 -0.5114 0.015 -33.597 0.000 -0.541 -0.482
ar.L16 -0.4605 0.015 -30.455 0.000 -0.490 -0.431
ar.L17 -0.4235 0.015 -28.651 0.000 -0.452 -0.395
ar.L18 -0.3818 0.015 -25.961 0.000 -0.411 -0.353
ar.L19 -0.3340 0.014 -23.188 0.000 -0.362 -0.306
ar.L20 -0.3361 0.014 -23.849 0.000 -0.364 -0.309
ar.L21 -0.3306 0.014 -23.778 0.000 -0.358 -0.303
ar.L22 -0.2992 0.013 -22.542 0.000 -0.325 -0.273
ar.L23 -0.2666 0.013 -20.656 0.000 -0.292 -0.241
ar.L24 -0.2367 0.012 -19.060 0.000 -0.261 -0.212
ar.L25 -0.1940 0.012 -16.739 0.000 -0.217 -0.171
ar.L26 -0.1492 0.011 -13.889 0.000 -0.170 -0.128
ar.L27 -0.1354 0.010 -13.991 0.000 -0.154 -0.116
ar.L28 -0.0971 0.008 -11.768 0.000 -0.113 -0.081
ar.L29 -0.0583 0.007 -8.206 0.000 -0.072 -0.044
ar.L30 -0.0397 0.005 -8.115 0.000 -0.049 -0.030
ma.L1 -1.0000 0.004 -234.226 0.000 -1.008 -0.992
sigma2 0.1498 0.001 215.525 0.000 0.148 0.151
===================================================================================
Ljung-Box (L1) (Q): 0.05 Jarque-Bera (JB): 510575.05
Prob(Q): 0.82 Prob(JB): 0.00
Heteroskedasticity (H): 8.78 Skew: 0.41
Prob(H) (two-sided): 0.00 Kurtosis: 29.16
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 119.211894
17891 119.376121
17892 119.420773
17893 119.541441
17894 119.469429
17895 119.385217
17896 119.355988
17897 119.245598
17898 119.246209
17899 119.143750
Name: predicted_mean, dtype: float64
lower price upper price
17890 118.453196 119.970592
17891 118.296015 120.456227
17892 118.089695 120.751851
17893 117.989428 121.093455
17894 117.710551 121.228307
17895 117.429150 121.341285
17896 117.205929 121.506047
17897 116.906713 121.584484
17898 116.723045 121.769373
17899 116.437451 121.850049
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 2.6269712520375847 Weighted Mean Absolute Percentage Error (WMAPE): 1.886995552488141
forecastplusyahoo('TKFEN', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 19.0042 TKFEN
2021-12-27 10:00:00+03:00 19.1636 TKFEN
2021-12-27 11:00:00+03:00 18.8271 TKFEN
2021-12-27 12:00:00+03:00 18.7563 TKFEN
2021-12-27 13:00:00+03:00 18.7563 TKFEN
... ... ...
2023-09-22 14:00:00+03:00 52.3000 TKFEN
2023-09-22 15:00:00+03:00 52.2000 TKFEN
2023-09-22 16:00:00+03:00 51.8000 TKFEN
2023-09-22 17:00:00+03:00 52.0000 TKFEN
2023-09-22 18:00:00+03:00 52.0000 TKFEN
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for TKFEN.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 19.004200
2021-12-27 06:30:00+00:00 21.580000
2021-12-27 07:00:00+00:00 19.163600
2021-12-27 07:30:00+00:00 21.360001
2021-12-27 08:00:00+00:00 18.827100
2021-12-27 08:30:00+00:00 21.280001
2021-12-27 09:00:00+00:00 18.756300
2021-12-27 09:30:00+00:00 21.120001
2021-12-27 10:00:00+00:00 18.756300
2021-12-27 10:30:00+00:00 21.160000
2021-12-27 11:00:00+00:00 18.579100
2021-12-27 11:30:00+00:00 20.900000
2021-12-27 12:00:00+00:00 18.614600
2021-12-27 12:30:00+00:00 20.879999
2021-12-27 13:00:00+00:00 18.579100
2021-12-27 13:30:00+00:00 21.059999
2021-12-27 14:00:00+00:00 18.508200
2021-12-27 15:00:00+00:00 18.437400
2021-12-28 06:00:00+00:00 18.596800
2021-12-28 06:30:00+00:00 20.780001
2021-12-28 07:00:00+00:00 18.490600
2021-12-28 07:30:00+00:00 20.840000
2021-12-28 08:00:00+00:00 18.402000
2021-12-28 08:30:00+00:00 20.620001
2021-12-28 09:00:00+00:00 18.278100
2021-12-28 09:30:00+00:00 20.580000
2021-12-28 10:00:00+00:00 18.047800
2021-12-28 10:30:00+00:00 20.400000
2021-12-28 11:00:00+00:00 17.959200
2021-12-28 11:30:00+00:00 20.400000
2021-12-28 12:00:00+00:00 18.136400
2021-12-28 12:30:00+00:00 20.340000
2021-12-28 13:00:00+00:00 17.906000
2021-12-28 13:30:00+00:00 19.799999
2021-12-28 14:00:00+00:00 17.711300
2021-12-28 15:00:00+00:00 17.658100
2021-12-29 06:00:00+00:00 17.569600
2021-12-29 06:30:00+00:00 19.719999
2021-12-29 07:00:00+00:00 17.498700
2021-12-29 07:30:00+00:00 20.139999
2021-12-29 08:00:00+00:00 17.941500
2021-12-29 08:30:00+00:00 20.180000
2021-12-29 09:00:00+00:00 17.976900
2021-12-29 09:30:00+00:00 20.280001
2021-12-29 10:00:00+00:00 18.171800
2021-12-29 10:30:00+00:00 20.280001
2021-12-29 11:00:00+00:00 18.100900
2021-12-29 11:30:00+00:00 20.600000
2021-12-29 12:00:00+00:00 18.189500
2021-12-29 12:30:00+00:00 20.780001
price
2023-09-21 10:00:00+00:00 47.880000
2023-09-21 10:30:00+00:00 48.459999
2023-09-21 11:00:00+00:00 48.920000
2023-09-21 11:30:00+00:00 49.220001
2023-09-21 12:00:00+00:00 49.080000
2023-09-21 12:30:00+00:00 49.380001
2023-09-21 13:00:00+00:00 50.450000
2023-09-21 13:30:00+00:00 50.950001
2023-09-21 14:00:00+00:00 50.950000
2023-09-21 14:30:00+00:00 50.950001
2023-09-21 15:00:00+00:00 50.950000
2023-09-22 06:00:00+00:00 53.800000
2023-09-22 06:30:00+00:00 52.849998
2023-09-22 07:00:00+00:00 52.550000
2023-09-22 07:30:00+00:00 52.750000
2023-09-22 08:00:00+00:00 52.350000
2023-09-22 08:30:00+00:00 52.450001
2023-09-22 09:00:00+00:00 52.300000
2023-09-22 09:30:00+00:00 52.200001
2023-09-22 10:00:00+00:00 52.550000
2023-09-22 10:30:00+00:00 52.349998
2023-09-22 11:00:00+00:00 52.300000
2023-09-22 11:30:00+00:00 51.849998
2023-09-22 12:00:00+00:00 52.200000
2023-09-22 12:30:00+00:00 52.049999
2023-09-22 13:00:00+00:00 51.800000
2023-09-22 13:30:00+00:00 51.950001
2023-09-22 14:00:00+00:00 52.000000
2023-09-22 14:30:00+00:00 52.000000
2023-09-22 15:00:00+00:00 52.000000
Mean of the first 10 values: price 52.09
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 52.20
2023-09-25 10:00:00+03:00 52.05
2023-09-25 11:00:00+03:00 51.90
2023-09-25 12:00:00+03:00 52.30
2023-09-25 13:00:00+03:00 52.25
2023-09-25 14:00:00+03:00 51.85
2023-09-25 15:00:00+03:00 52.00
2023-09-25 16:00:00+03:00 52.00
2023-09-25 17:00:00+03:00 52.05
2023-09-25 18:00:00+03:00 52.30
price
2018-01-02 10:00:00+03:00 0.060000
2018-01-02 11:00:00+03:00 -0.097400
2018-01-02 12:00:00+03:00 -0.112300
2018-01-02 13:00:00+03:00 0.000000
2018-01-02 14:00:00+03:00 0.007500
... ...
2023-09-22 13:00:00+00:00 -0.249999
2023-09-22 13:30:00+00:00 0.150001
2023-09-22 14:00:00+00:00 0.049999
2023-09-22 14:30:00+00:00 0.000000
2023-09-22 15:00:00+00:00 0.000000
[17890 rows x 1 columns]
ADF Statistic: -20.287454739221047
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17891
Model: ARIMA(30, 1, 1) Log Likelihood -11183.700
Date: Sun, 24 Dec 2023 AIC 22431.399
Time: 22:10:23 BIC 22680.743
Sample: 0 HQIC 22513.419
- 17891
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.4903 0.276 -1.775 0.076 -1.032 0.051
ar.L2 0.2500 0.257 0.974 0.330 -0.253 0.753
ar.L3 0.0211 0.044 0.484 0.629 -0.064 0.107
ar.L4 0.0207 0.014 1.442 0.149 -0.007 0.049
ar.L5 -0.0563 0.006 -8.722 0.000 -0.069 -0.044
ar.L6 0.0128 0.017 0.742 0.458 -0.021 0.046
ar.L7 -0.0299 0.008 -3.809 0.000 -0.045 -0.015
ar.L8 0.0181 0.013 1.428 0.153 -0.007 0.043
ar.L9 -0.0309 0.008 -4.096 0.000 -0.046 -0.016
ar.L10 0.0431 0.011 3.957 0.000 0.022 0.064
ar.L11 -0.0007 0.010 -0.069 0.945 -0.020 0.019
ar.L12 -0.0306 0.008 -3.802 0.000 -0.046 -0.015
ar.L13 -0.0204 0.008 -2.612 0.009 -0.036 -0.005
ar.L14 -0.0435 0.010 -4.579 0.000 -0.062 -0.025
ar.L15 -0.0193 0.017 -1.145 0.252 -0.052 0.014
ar.L16 -0.1756 0.013 -13.652 0.000 -0.201 -0.150
ar.L17 -0.0292 0.054 -0.540 0.589 -0.135 0.077
ar.L18 0.4408 0.032 13.740 0.000 0.378 0.504
ar.L19 0.3749 0.108 3.481 0.000 0.164 0.586
ar.L20 -0.2882 0.151 -1.913 0.056 -0.584 0.007
ar.L21 -0.1780 0.014 -12.283 0.000 -0.206 -0.150
ar.L22 -0.0015 0.056 -0.026 0.979 -0.110 0.107
ar.L23 -0.0386 0.025 -1.532 0.125 -0.088 0.011
ar.L24 0.0227 0.022 1.025 0.305 -0.021 0.066
ar.L25 -0.0385 0.008 -4.929 0.000 -0.054 -0.023
ar.L26 0.0255 0.014 1.851 0.064 -0.002 0.053
ar.L27 -0.0322 0.008 -4.076 0.000 -0.048 -0.017
ar.L28 0.0010 0.012 0.083 0.934 -0.022 0.024
ar.L29 -0.0614 0.007 -8.556 0.000 -0.075 -0.047
ar.L30 0.0519 0.020 2.540 0.011 0.012 0.092
ma.L1 -0.4376 0.276 -1.585 0.113 -0.979 0.103
sigma2 0.2040 0.001 206.976 0.000 0.202 0.206
===================================================================================
Ljung-Box (L1) (Q): 0.14 Jarque-Bera (JB): 123759.96
Prob(Q): 0.71 Prob(JB): 0.00
Heteroskedasticity (H): 12.60 Skew: -1.00
Prob(H) (two-sided): 0.00 Kurtosis: 15.73
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891 53.083374
17892 51.299178
17893 52.164979
17894 51.497429
17895 51.807338
17896 51.443712
17897 51.712360
17898 51.662947
17899 51.969321
17900 51.473314
Name: predicted_mean, dtype: float64
lower price upper price
17891 52.198086 53.968662
17892 50.411594 52.186762
17893 51.042026 53.287932
17894 50.357637 52.637222
17895 50.522138 53.092538
17896 50.139571 52.747853
17897 50.291568 53.133152
17898 50.223832 53.102061
17899 50.426741 53.511902
17900 49.914911 53.031718
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.5749518552037074 Weighted Mean Absolute Percentage Error (WMAPE): 0.9672679786837399
forecastplusyahoo('TUPRS',30,1,3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 19.6507 TUPRS
2021-12-27 10:00:00+03:00 20.4990 TUPRS
2021-12-27 11:00:00+03:00 20.2331 TUPRS
2021-12-27 12:00:00+03:00 19.9926 TUPRS
2021-12-27 13:00:00+03:00 20.0052 TUPRS
... ... ...
2023-09-22 14:00:00+03:00 145.8839 TUPRS
2023-09-22 15:00:00+03:00 147.0288 TUPRS
2023-09-22 16:00:00+03:00 147.8875 TUPRS
2023-09-22 17:00:00+03:00 148.0784 TUPRS
2023-09-22 18:00:00+03:00 147.0288 TUPRS
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for TUPRS.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 19.650700
2021-12-27 06:30:00+00:00 22.614286
2021-12-27 07:00:00+00:00 20.499000
2021-12-27 07:30:00+00:00 22.842855
2021-12-27 08:00:00+00:00 20.233100
2021-12-27 08:30:00+00:00 22.685715
2021-12-27 09:00:00+00:00 19.992600
2021-12-27 09:30:00+00:00 22.414284
2021-12-27 10:00:00+00:00 20.005200
2021-12-27 10:30:00+00:00 22.471428
2021-12-27 11:00:00+00:00 19.714000
2021-12-27 11:30:00+00:00 22.171429
2021-12-27 12:00:00+00:00 19.688700
2021-12-27 12:30:00+00:00 22.114286
2021-12-27 13:00:00+00:00 19.600100
2021-12-27 13:30:00+00:00 22.199999
2021-12-27 14:00:00+00:00 19.346800
2021-12-27 15:00:00+00:00 19.346800
2021-12-28 06:00:00+00:00 19.752000
2021-12-28 06:30:00+00:00 22.057142
2021-12-28 07:00:00+00:00 19.777300
2021-12-28 07:30:00+00:00 22.057142
2021-12-28 08:00:00+00:00 19.574700
2021-12-28 08:30:00+00:00 22.171429
2021-12-28 09:00:00+00:00 19.587400
2021-12-28 09:30:00+00:00 22.142857
2021-12-28 10:00:00+00:00 19.524100
2021-12-28 10:30:00+00:00 22.100000
2021-12-28 11:00:00+00:00 19.422800
2021-12-28 11:30:00+00:00 21.914284
2021-12-28 12:00:00+00:00 19.384800
2021-12-28 12:30:00+00:00 21.814285
2021-12-28 13:00:00+00:00 19.207500
2021-12-28 13:30:00+00:00 21.228573
2021-12-28 14:00:00+00:00 18.928900
2021-12-28 15:00:00+00:00 18.941600
2021-12-29 06:00:00+00:00 18.941600
2021-12-29 06:30:00+00:00 21.199999
2021-12-29 07:00:00+00:00 18.865700
2021-12-29 07:30:00+00:00 21.614286
2021-12-29 08:00:00+00:00 19.220200
2021-12-29 08:30:00+00:00 21.828571
2021-12-29 09:00:00+00:00 19.384800
2021-12-29 09:30:00+00:00 22.042856
2021-12-29 10:00:00+00:00 19.574700
2021-12-29 10:30:00+00:00 21.957144
2021-12-29 11:00:00+00:00 19.612700
2021-12-29 11:30:00+00:00 22.257143
2021-12-29 12:00:00+00:00 19.650700
2021-12-29 12:30:00+00:00 22.342855
price
2023-09-21 10:00:00+00:00 134.530000
2023-09-21 10:30:00+00:00 141.600006
2023-09-21 11:00:00+00:00 137.583100
2023-09-21 11:30:00+00:00 144.699997
2023-09-21 12:00:00+00:00 138.155600
2023-09-21 12:30:00+00:00 146.600006
2023-09-21 13:00:00+00:00 139.586800
2023-09-21 13:30:00+00:00 147.100006
2023-09-21 14:00:00+00:00 141.017900
2023-09-21 14:30:00+00:00 147.800003
2023-09-21 15:00:00+00:00 141.017900
2023-09-22 06:00:00+00:00 141.208700
2023-09-22 06:30:00+00:00 154.600006
2023-09-22 07:00:00+00:00 145.788500
2023-09-22 07:30:00+00:00 152.899994
2023-09-22 08:00:00+00:00 146.361000
2023-09-22 08:30:00+00:00 153.500000
2023-09-22 09:00:00+00:00 147.410500
2023-09-22 09:30:00+00:00 153.899994
2023-09-22 10:00:00+00:00 146.647200
2023-09-22 10:30:00+00:00 153.000000
2023-09-22 11:00:00+00:00 145.883900
2023-09-22 11:30:00+00:00 152.899994
2023-09-22 12:00:00+00:00 147.028800
2023-09-22 12:30:00+00:00 154.699997
2023-09-22 13:00:00+00:00 147.887500
2023-09-22 13:30:00+00:00 154.500000
2023-09-22 14:00:00+00:00 148.078400
2023-09-22 14:30:00+00:00 155.199997
2023-09-22 15:00:00+00:00 147.028800
Mean of the first 10 values: price 155.96888
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 150.0820
2023-09-25 10:00:00+03:00 153.3260
2023-09-25 11:00:00+03:00 154.8526
2023-09-25 12:00:00+03:00 157.2379
2023-09-25 13:00:00+03:00 156.2837
2023-09-25 14:00:00+03:00 155.9975
2023-09-25 15:00:00+03:00 157.8103
2023-09-25 16:00:00+03:00 156.8562
2023-09-25 17:00:00+03:00 158.8598
2023-09-25 18:00:00+03:00 158.3828
price
2018-01-02 10:00:00+03:00 0.070300
2018-01-02 11:00:00+03:00 -0.020100
2018-01-02 12:00:00+03:00 0.010100
2018-01-02 13:00:00+03:00 0.020000
2018-01-02 14:00:00+03:00 0.030200
... ...
2023-09-22 13:00:00+00:00 -6.812497
2023-09-22 13:30:00+00:00 6.612500
2023-09-22 14:00:00+00:00 -6.421600
2023-09-22 14:30:00+00:00 7.121597
2023-09-22 15:00:00+00:00 -8.171197
[17890 rows x 1 columns]
ADF Statistic: -19.38840832103753
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17891
Model: ARIMA(30, 3, 1) Log Likelihood -24997.129
Date: Sun, 24 Dec 2023 AIC 50058.258
Time: 22:13:30 BIC 50307.599
Sample: 0 HQIC 50140.278
- 17891
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.9439 0.011 -184.448 0.000 -1.965 -1.923
ar.L2 -2.0630 0.030 -69.270 0.000 -2.121 -2.005
ar.L3 -1.9327 0.049 -39.638 0.000 -2.028 -1.837
ar.L4 -1.6895 0.065 -25.898 0.000 -1.817 -1.562
ar.L5 -1.4618 0.079 -18.566 0.000 -1.616 -1.307
ar.L6 -1.1725 0.090 -13.076 0.000 -1.348 -0.997
ar.L7 -0.9419 0.097 -9.672 0.000 -1.133 -0.751
ar.L8 -0.7507 0.103 -7.308 0.000 -0.952 -0.549
ar.L9 -0.6409 0.106 -6.036 0.000 -0.849 -0.433
ar.L10 -0.4753 0.109 -4.366 0.000 -0.689 -0.262
ar.L11 -0.2882 0.109 -2.633 0.008 -0.503 -0.074
ar.L12 -0.0651 0.108 -0.601 0.548 -0.277 0.147
ar.L13 0.0991 0.106 0.934 0.350 -0.109 0.307
ar.L14 0.1707 0.103 1.661 0.097 -0.031 0.372
ar.L15 0.1959 0.099 1.981 0.048 0.002 0.390
ar.L16 0.0956 0.095 1.005 0.315 -0.091 0.282
ar.L17 0.1754 0.093 1.890 0.059 -0.007 0.357
ar.L18 0.7820 0.090 8.714 0.000 0.606 0.958
ar.L19 1.6629 0.081 20.557 0.000 1.504 1.821
ar.L20 1.8672 0.065 28.877 0.000 1.740 1.994
ar.L21 1.6554 0.049 34.024 0.000 1.560 1.751
ar.L22 1.4055 0.037 38.034 0.000 1.333 1.478
ar.L23 1.1273 0.030 37.215 0.000 1.068 1.187
ar.L24 0.9067 0.027 33.486 0.000 0.854 0.960
ar.L25 0.6717 0.025 26.830 0.000 0.623 0.721
ar.L26 0.5586 0.023 24.786 0.000 0.514 0.603
ar.L27 0.4730 0.020 23.884 0.000 0.434 0.512
ar.L28 0.4042 0.017 23.856 0.000 0.371 0.437
ar.L29 0.2577 0.013 20.109 0.000 0.233 0.283
ar.L30 0.1201 0.006 20.260 0.000 0.109 0.132
ma.L1 -0.9423 0.009 -102.775 0.000 -0.960 -0.924
sigma2 0.9511 0.005 210.597 0.000 0.942 0.960
===================================================================================
Ljung-Box (L1) (Q): 29.99 Jarque-Bera (JB): 220509.09
Prob(Q): 0.00 Prob(JB): 0.00
Heteroskedasticity (H): 78.74 Skew: -0.83
Prob(H) (two-sided): 0.00 Kurtosis: 20.12
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891 157.133317
17892 156.276642
17893 152.934216
17894 155.769074
17895 152.244440
17896 156.116036
17897 151.898937
17898 154.619788
17899 150.415592
17900 153.223418
Name: predicted_mean, dtype: float64
lower price upper price
17891 155.221892 159.044743
17892 154.352869 158.200415
17893 150.367638 155.500794
17894 153.061658 158.476489
17895 148.976546 155.512335
17896 152.583161 159.648911
17897 147.728434 156.069440
17898 150.089144 159.150433
17899 145.200584 155.630600
17900 147.571094 158.875742
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 4.674016512569116 Weighted Mean Absolute Percentage Error (WMAPE): 2.0122977706869536
forecastplusyahoo('TTKOM', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 9.3416 TTKOM
2021-12-27 10:00:00+03:00 9.3416 TTKOM
2021-12-27 11:00:00+03:00 9.3061 TTKOM
2021-12-27 12:00:00+03:00 9.2795 TTKOM
2021-12-27 13:00:00+03:00 9.3061 TTKOM
... ... ...
2023-09-22 14:00:00+03:00 23.2200 TTKOM
2023-09-22 15:00:00+03:00 23.1800 TTKOM
2023-09-22 16:00:00+03:00 23.2200 TTKOM
2023-09-22 17:00:00+03:00 23.2000 TTKOM
2023-09-22 18:00:00+03:00 23.1800 TTKOM
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for TTKOM.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 9.3416
2021-12-27 06:30:00+00:00 10.5200
2021-12-27 07:00:00+00:00 9.3416
2021-12-27 07:30:00+00:00 10.5100
2021-12-27 08:00:00+00:00 9.3061
2021-12-27 08:30:00+00:00 10.5100
2021-12-27 09:00:00+00:00 9.2795
2021-12-27 09:30:00+00:00 10.4400
2021-12-27 10:00:00+00:00 9.3061
2021-12-27 10:30:00+00:00 10.4900
2021-12-27 11:00:00+00:00 9.1732
2021-12-27 11:30:00+00:00 10.2800
2021-12-27 12:00:00+00:00 9.0934
2021-12-27 12:30:00+00:00 10.3100
2021-12-27 13:00:00+00:00 9.1554
2021-12-27 13:30:00+00:00 10.3500
2021-12-27 14:00:00+00:00 9.1377
2021-12-27 15:00:00+00:00 9.1200
2021-12-28 06:00:00+00:00 9.1023
2021-12-28 06:30:00+00:00 10.3700
2021-12-28 07:00:00+00:00 9.2086
2021-12-28 07:30:00+00:00 10.4100
2021-12-28 08:00:00+00:00 9.1998
2021-12-28 08:30:00+00:00 10.3500
2021-12-28 09:00:00+00:00 9.1466
2021-12-28 09:30:00+00:00 10.3100
2021-12-28 10:00:00+00:00 9.1377
2021-12-28 10:30:00+00:00 10.2800
2021-12-28 11:00:00+00:00 9.0402
2021-12-28 11:30:00+00:00 10.2300
2021-12-28 12:00:00+00:00 9.0668
2021-12-28 12:30:00+00:00 10.2000
2021-12-28 13:00:00+00:00 8.9605
2021-12-28 13:30:00+00:00 9.8800
2021-12-28 14:00:00+00:00 8.7566
2021-12-28 15:00:00+00:00 8.7300
2021-12-29 06:00:00+00:00 8.7123
2021-12-29 06:30:00+00:00 9.7900
2021-12-29 07:00:00+00:00 8.7212
2021-12-29 07:30:00+00:00 9.8800
2021-12-29 08:00:00+00:00 8.7832
2021-12-29 08:30:00+00:00 9.9600
2021-12-29 09:00:00+00:00 8.8364
2021-12-29 09:30:00+00:00 9.9200
2021-12-29 10:00:00+00:00 8.8187
2021-12-29 10:30:00+00:00 9.8700
2021-12-29 11:00:00+00:00 8.7655
2021-12-29 11:30:00+00:00 9.9500
2021-12-29 12:00:00+00:00 8.8541
2021-12-29 12:30:00+00:00 10.0500
price
2023-09-21 10:00:00+00:00 22.520000
2023-09-21 10:30:00+00:00 22.820000
2023-09-21 11:00:00+00:00 23.120000
2023-09-21 11:30:00+00:00 22.980000
2023-09-21 12:00:00+00:00 22.940000
2023-09-21 12:30:00+00:00 23.219999
2023-09-21 13:00:00+00:00 22.980000
2023-09-21 13:30:00+00:00 23.139999
2023-09-21 14:00:00+00:00 23.260000
2023-09-21 14:30:00+00:00 23.260000
2023-09-21 15:00:00+00:00 23.340000
2023-09-22 06:00:00+00:00 23.380000
2023-09-22 06:30:00+00:00 23.280001
2023-09-22 07:00:00+00:00 23.340000
2023-09-22 07:30:00+00:00 23.620001
2023-09-22 08:00:00+00:00 23.620000
2023-09-22 08:30:00+00:00 23.500000
2023-09-22 09:00:00+00:00 23.480000
2023-09-22 09:30:00+00:00 23.360001
2023-09-22 10:00:00+00:00 23.420000
2023-09-22 10:30:00+00:00 23.320000
2023-09-22 11:00:00+00:00 23.220000
2023-09-22 11:30:00+00:00 23.080000
2023-09-22 12:00:00+00:00 23.180000
2023-09-22 12:30:00+00:00 23.260000
2023-09-22 13:00:00+00:00 23.220000
2023-09-22 13:30:00+00:00 23.160000
2023-09-22 14:00:00+00:00 23.200000
2023-09-22 14:30:00+00:00 23.200001
2023-09-22 15:00:00+00:00 23.180000
Mean of the first 10 values: price 23.638
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 23.40
2023-09-25 10:00:00+03:00 23.62
2023-09-25 11:00:00+03:00 23.52
2023-09-25 12:00:00+03:00 23.70
2023-09-25 13:00:00+03:00 23.70
2023-09-25 14:00:00+03:00 23.52
2023-09-25 15:00:00+03:00 23.48
2023-09-25 16:00:00+03:00 23.56
2023-09-25 17:00:00+03:00 23.94
2023-09-25 18:00:00+03:00 23.94
price
2018-01-02 10:00:00+03:00 1.123000e-01
2018-01-02 11:00:00+03:00 0.000000e+00
2018-01-02 12:00:00+03:00 -1.610000e-02
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 2.410000e-02
... ...
2023-09-22 13:00:00+00:00 -4.000023e-02
2023-09-22 13:30:00+00:00 -6.000015e-02
2023-09-22 14:00:00+00:00 4.000015e-02
2023-09-22 14:30:00+00:00 7.629395e-07
2023-09-22 15:00:00+00:00 -2.000076e-02
[17890 rows x 1 columns]
ADF Statistic: -19.370951544217785
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17891
Model: ARIMA(30, 3, 1) Log Likelihood 11772.101
Date: Sun, 24 Dec 2023 AIC -23480.202
Time: 22:30:19 BIC -23230.862
Sample: 0 HQIC -23398.183
- 17891
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.2435 0.004 -303.873 0.000 -1.252 -1.236
ar.L2 -1.2185 0.008 -146.330 0.000 -1.235 -1.202
ar.L3 -1.2852 0.012 -111.606 0.000 -1.308 -1.263
ar.L4 -1.2370 0.015 -83.193 0.000 -1.266 -1.208
ar.L5 -1.2729 0.018 -72.414 0.000 -1.307 -1.238
ar.L6 -1.2313 0.020 -61.552 0.000 -1.270 -1.192
ar.L7 -1.2545 0.022 -57.520 0.000 -1.297 -1.212
ar.L8 -1.2125 0.024 -51.462 0.000 -1.259 -1.166
ar.L9 -1.1984 0.025 -47.762 0.000 -1.248 -1.149
ar.L10 -1.1309 0.026 -42.914 0.000 -1.183 -1.079
ar.L11 -1.1024 0.027 -40.625 0.000 -1.156 -1.049
ar.L12 -1.0544 0.028 -38.155 0.000 -1.109 -1.000
ar.L13 -1.0281 0.028 -36.817 0.000 -1.083 -0.973
ar.L14 -0.9734 0.027 -35.461 0.000 -1.027 -0.920
ar.L15 -0.9387 0.027 -34.375 0.000 -0.992 -0.885
ar.L16 -0.9540 0.027 -35.167 0.000 -1.007 -0.901
ar.L17 -1.0646 0.027 -39.449 0.000 -1.117 -1.012
ar.L18 -0.5202 0.027 -19.117 0.000 -0.574 -0.467
ar.L19 -0.4939 0.026 -19.046 0.000 -0.545 -0.443
ar.L20 -0.5683 0.025 -23.167 0.000 -0.616 -0.520
ar.L21 -0.5275 0.023 -22.693 0.000 -0.573 -0.482
ar.L22 -0.5095 0.022 -23.332 0.000 -0.552 -0.467
ar.L23 -0.4232 0.021 -20.628 0.000 -0.463 -0.383
ar.L24 -0.3709 0.019 -19.676 0.000 -0.408 -0.334
ar.L25 -0.2738 0.017 -15.911 0.000 -0.308 -0.240
ar.L26 -0.2128 0.015 -13.756 0.000 -0.243 -0.182
ar.L27 -0.1560 0.013 -11.576 0.000 -0.182 -0.130
ar.L28 -0.1267 0.011 -11.590 0.000 -0.148 -0.105
ar.L29 -0.0822 0.009 -9.622 0.000 -0.099 -0.065
ar.L30 -0.0368 0.005 -7.279 0.000 -0.047 -0.027
ma.L1 -0.9355 0.003 -300.826 0.000 -0.942 -0.929
sigma2 0.0157 5.5e-05 285.916 0.000 0.016 0.016
===================================================================================
Ljung-Box (L1) (Q): 0.18 Jarque-Bera (JB): 483315.68
Prob(Q): 0.68 Prob(JB): 0.00
Heteroskedasticity (H): 14.17 Skew: 0.64
Prob(H) (two-sided): 0.00 Kurtosis: 28.43
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891 23.118978
17892 23.141193
17893 23.307552
17894 23.292809
17895 23.215727
17896 23.228411
17897 23.168260
17898 23.238292
17899 23.207234
17900 23.176528
Name: predicted_mean, dtype: float64
lower price upper price
17891 22.873296 23.364661
17892 22.823317 23.459068
17893 22.912594 23.702510
17894 22.838350 23.747269
17895 22.692848 23.738606
17896 22.647597 23.809224
17897 22.521299 23.815221
17898 22.532760 23.943823
17899 22.434982 23.979487
17900 22.341099 24.011956
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.46467470284892237 Weighted Mean Absolute Percentage Error (WMAPE): 1.812765818379819
forecastplusyahoo('TCELL', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 18.8440 TCELL
2021-12-27 10:00:00+03:00 18.9601 TCELL
2021-12-27 11:00:00+03:00 18.7569 TCELL
2021-12-27 12:00:00+03:00 18.7182 TCELL
2021-12-27 13:00:00+03:00 18.7763 TCELL
... ... ...
2023-09-22 14:00:00+03:00 54.4000 TCELL
2023-09-22 15:00:00+03:00 54.5000 TCELL
2023-09-22 16:00:00+03:00 54.5500 TCELL
2023-09-22 17:00:00+03:00 54.1000 TCELL
2023-09-22 18:00:00+03:00 54.4500 TCELL
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for TCELL.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 18.844000
2021-12-27 06:30:00+00:00 19.480000
2021-12-27 07:00:00+00:00 18.960100
2021-12-27 07:30:00+00:00 19.520000
2021-12-27 08:00:00+00:00 18.756900
2021-12-27 08:30:00+00:00 19.400000
2021-12-27 09:00:00+00:00 18.718200
2021-12-27 09:30:00+00:00 19.270000
2021-12-27 10:00:00+00:00 18.776300
2021-12-27 10:30:00+00:00 19.330000
2021-12-27 11:00:00+00:00 18.253600
2021-12-27 11:30:00+00:00 18.709999
2021-12-27 12:00:00+00:00 18.331000
2021-12-27 12:30:00+00:00 19.000000
2021-12-27 13:00:00+00:00 18.534300
2021-12-27 13:30:00+00:00 19.299999
2021-12-27 14:00:00+00:00 18.408500
2021-12-27 15:00:00+00:00 18.292300
2021-12-28 06:00:00+00:00 18.456900
2021-12-28 06:30:00+00:00 19.090000
2021-12-28 07:00:00+00:00 18.698800
2021-12-28 07:30:00+00:00 19.350000
2021-12-28 08:00:00+00:00 18.602000
2021-12-28 08:30:00+00:00 19.200001
2021-12-28 09:00:00+00:00 18.631100
2021-12-28 09:30:00+00:00 19.219999
2021-12-28 10:00:00+00:00 18.563300
2021-12-28 10:30:00+00:00 19.090000
2021-12-28 11:00:00+00:00 18.524600
2021-12-28 11:30:00+00:00 19.219999
2021-12-28 12:00:00+00:00 18.534300
2021-12-28 12:30:00+00:00 19.180000
2021-12-28 13:00:00+00:00 18.524600
2021-12-28 13:30:00+00:00 18.809999
2021-12-28 14:00:00+00:00 18.098800
2021-12-28 15:00:00+00:00 18.002000
2021-12-29 06:00:00+00:00 18.002000
2021-12-29 06:30:00+00:00 18.740000
2021-12-29 07:00:00+00:00 18.166500
2021-12-29 07:30:00+00:00 18.879999
2021-12-29 08:00:00+00:00 18.389100
2021-12-29 08:30:00+00:00 18.980000
2021-12-29 09:00:00+00:00 18.389100
2021-12-29 09:30:00+00:00 18.990000
2021-12-29 10:00:00+00:00 18.379400
2021-12-29 10:30:00+00:00 18.920000
2021-12-29 11:00:00+00:00 18.408500
2021-12-29 11:30:00+00:00 19.219999
2021-12-29 12:00:00+00:00 18.737500
2021-12-29 12:30:00+00:00 19.490000
price
2023-09-21 10:00:00+00:00 53.150000
2023-09-21 10:30:00+00:00 53.849998
2023-09-21 11:00:00+00:00 54.750000
2023-09-21 11:30:00+00:00 54.349998
2023-09-21 12:00:00+00:00 54.350000
2023-09-21 12:30:00+00:00 55.049999
2023-09-21 13:00:00+00:00 54.400000
2023-09-21 13:30:00+00:00 55.150002
2023-09-21 14:00:00+00:00 55.400000
2023-09-21 14:30:00+00:00 55.400002
2023-09-21 15:00:00+00:00 55.400000
2023-09-22 06:00:00+00:00 55.400000
2023-09-22 06:30:00+00:00 55.099998
2023-09-22 07:00:00+00:00 54.850000
2023-09-22 07:30:00+00:00 54.950001
2023-09-22 08:00:00+00:00 54.750000
2023-09-22 08:30:00+00:00 54.799999
2023-09-22 09:00:00+00:00 54.750000
2023-09-22 09:30:00+00:00 54.549999
2023-09-22 10:00:00+00:00 54.600000
2023-09-22 10:30:00+00:00 54.400002
2023-09-22 11:00:00+00:00 54.400000
2023-09-22 11:30:00+00:00 54.250000
2023-09-22 12:00:00+00:00 54.500000
2023-09-22 12:30:00+00:00 54.400002
2023-09-22 13:00:00+00:00 54.550000
2023-09-22 13:30:00+00:00 54.200001
2023-09-22 14:00:00+00:00 54.100000
2023-09-22 14:30:00+00:00 54.099998
2023-09-22 15:00:00+00:00 54.450000
Mean of the first 10 values: price 54.815
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 54.60
2023-09-25 10:00:00+03:00 54.60
2023-09-25 11:00:00+03:00 54.45
2023-09-25 12:00:00+03:00 54.85
2023-09-25 13:00:00+03:00 54.95
2023-09-25 14:00:00+03:00 54.85
2023-09-25 15:00:00+03:00 54.65
2023-09-25 16:00:00+03:00 55.00
2023-09-25 17:00:00+03:00 55.00
2023-09-25 18:00:00+03:00 55.20
price
2018-01-02 10:00:00+03:00 0.039200
2018-01-02 11:00:00+03:00 -0.101800
2018-01-02 12:00:00+03:00 -0.007700
2018-01-02 13:00:00+03:00 0.054800
2018-01-02 14:00:00+03:00 0.054700
... ...
2023-09-22 13:00:00+00:00 0.149998
2023-09-22 13:30:00+00:00 -0.349999
2023-09-22 14:00:00+00:00 -0.100001
2023-09-22 14:30:00+00:00 -0.000002
2023-09-22 15:00:00+00:00 0.350002
[17890 rows x 1 columns]
ADF Statistic: -21.755813672308253
p-value: 0.0
Critical Values: {'1%': -3.4307163795075204, '5%': -2.8617019257919023, '10%': -2.5668561888610237}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17891
Model: ARIMA(30, 1, 1) Log Likelihood 3489.444
Date: Sun, 24 Dec 2023 AIC -6914.888
Time: 22:26:11 BIC -6665.544
Sample: 0 HQIC -6832.868
- 17891
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.0701 0.005 -223.985 0.000 -1.079 -1.061
ar.L2 -0.1050 0.005 -22.096 0.000 -0.114 -0.096
ar.L3 -0.0284 0.005 -6.158 0.000 -0.037 -0.019
ar.L4 -0.0506 0.006 -8.899 0.000 -0.062 -0.039
ar.L5 -0.0518 0.007 -7.738 0.000 -0.065 -0.039
ar.L6 -0.0250 0.007 -3.630 0.000 -0.038 -0.011
ar.L7 -0.0038 0.007 -0.538 0.591 -0.018 0.010
ar.L8 -0.0069 0.007 -0.997 0.319 -0.021 0.007
ar.L9 0.0064 0.007 0.963 0.336 -0.007 0.019
ar.L10 0.0012 0.007 0.176 0.860 -0.012 0.015
ar.L11 0.0222 0.006 3.454 0.001 0.010 0.035
ar.L12 0.0035 0.007 0.540 0.589 -0.009 0.016
ar.L13 -0.0062 0.007 -0.937 0.349 -0.019 0.007
ar.L14 0.0018 0.006 0.304 0.761 -0.010 0.013
ar.L15 0.0159 0.006 2.741 0.006 0.005 0.027
ar.L16 -0.0191 0.006 -3.265 0.001 -0.031 -0.008
ar.L17 -0.0533 0.005 -10.428 0.000 -0.063 -0.043
ar.L18 0.1099 0.005 21.499 0.000 0.100 0.120
ar.L19 0.1080 0.004 25.429 0.000 0.100 0.116
ar.L20 -0.0784 0.005 -15.125 0.000 -0.089 -0.068
ar.L21 -0.0675 0.005 -12.637 0.000 -0.078 -0.057
ar.L22 -0.0094 0.006 -1.647 0.100 -0.021 0.002
ar.L23 0.0235 0.006 3.945 0.000 0.012 0.035
ar.L24 0.0293 0.006 4.976 0.000 0.018 0.041
ar.L25 0.0204 0.007 3.124 0.002 0.008 0.033
ar.L26 0.0203 0.007 2.787 0.005 0.006 0.035
ar.L27 -0.0101 0.007 -1.426 0.154 -0.024 0.004
ar.L28 -0.0136 0.007 -1.933 0.053 -0.027 0.000
ar.L29 -0.0091 0.007 -1.245 0.213 -0.024 0.005
ar.L30 0.0341 0.005 6.251 0.000 0.023 0.045
ma.L1 0.9294 0.003 271.900 0.000 0.923 0.936
sigma2 0.0397 0.000 286.185 0.000 0.039 0.040
===================================================================================
Ljung-Box (L1) (Q): 2.38 Jarque-Bera (JB): 350209.07
Prob(Q): 0.12 Prob(JB): 0.00
Heteroskedasticity (H): 15.78 Skew: 0.71
Prob(H) (two-sided): 0.00 Kurtosis: 24.63
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891 54.353134
17892 54.423341
17893 54.415687
17894 54.429490
17895 54.411605
17896 54.448196
17897 54.343791
17898 54.448544
17899 54.349230
17900 54.433034
Name: predicted_mean, dtype: float64
lower price upper price
17891 53.962407 54.743862
17892 53.908178 54.938504
17893 53.790862 55.040512
17894 53.723253 55.135728
17895 53.629862 55.193348
17896 53.605983 55.290409
17897 53.439609 55.247972
17898 53.491200 55.405889
17899 53.337587 55.360874
17900 53.372286 55.493782
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.46163604567845706 Weighted Mean Absolute Percentage Error (WMAPE): 0.7468662607384804
forecastplusyahoo('HALKB', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 4.78 HALKB
2021-12-27 10:00:00+03:00 4.80 HALKB
2021-12-27 11:00:00+03:00 4.78 HALKB
2021-12-27 12:00:00+03:00 4.78 HALKB
2021-12-27 13:00:00+03:00 4.78 HALKB
... ... ...
2023-09-22 14:00:00+03:00 15.36 HALKB
2023-09-22 15:00:00+03:00 15.32 HALKB
2023-09-22 16:00:00+03:00 15.32 HALKB
2023-09-22 17:00:00+03:00 15.20 HALKB
2023-09-22 18:00:00+03:00 15.23 HALKB
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for HALKB.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 4.78
2021-12-27 06:30:00+00:00 4.78
2021-12-27 07:00:00+00:00 4.80
2021-12-27 07:30:00+00:00 4.79
2021-12-27 08:00:00+00:00 4.78
2021-12-27 08:30:00+00:00 4.79
2021-12-27 09:00:00+00:00 4.78
2021-12-27 09:30:00+00:00 4.76
2021-12-27 10:00:00+00:00 4.78
2021-12-27 10:30:00+00:00 4.78
2021-12-27 11:00:00+00:00 4.74
2021-12-27 11:30:00+00:00 4.72
2021-12-27 12:00:00+00:00 4.73
2021-12-27 12:30:00+00:00 4.72
2021-12-27 13:00:00+00:00 4.74
2021-12-27 13:30:00+00:00 4.72
2021-12-27 14:00:00+00:00 4.70
2021-12-27 15:00:00+00:00 4.70
2021-12-28 06:00:00+00:00 4.71
2021-12-28 06:30:00+00:00 4.66
2021-12-28 07:00:00+00:00 4.67
2021-12-28 07:30:00+00:00 4.66
2021-12-28 08:00:00+00:00 4.65
2021-12-28 08:30:00+00:00 4.65
2021-12-28 09:00:00+00:00 4.66
2021-12-28 09:30:00+00:00 4.64
2021-12-28 10:00:00+00:00 4.66
2021-12-28 10:30:00+00:00 4.65
2021-12-28 11:00:00+00:00 4.64
2021-12-28 11:30:00+00:00 4.66
2021-12-28 12:00:00+00:00 4.65
2021-12-28 12:30:00+00:00 4.65
2021-12-28 13:00:00+00:00 4.64
2021-12-28 13:30:00+00:00 4.57
2021-12-28 14:00:00+00:00 4.57
2021-12-28 15:00:00+00:00 4.58
2021-12-29 06:00:00+00:00 4.58
2021-12-29 06:30:00+00:00 4.55
2021-12-29 07:00:00+00:00 4.55
2021-12-29 07:30:00+00:00 4.59
2021-12-29 08:00:00+00:00 4.61
2021-12-29 08:30:00+00:00 4.59
2021-12-29 09:00:00+00:00 4.58
2021-12-29 09:30:00+00:00 4.58
2021-12-29 10:00:00+00:00 4.60
2021-12-29 10:30:00+00:00 4.58
2021-12-29 11:00:00+00:00 4.59
2021-12-29 11:30:00+00:00 4.61
2021-12-29 12:00:00+00:00 4.66
2021-12-29 12:30:00+00:00 4.66
price
2023-09-21 10:00:00+00:00 15.66
2023-09-21 10:30:00+00:00 15.24
2023-09-21 11:00:00+00:00 15.34
2023-09-21 11:30:00+00:00 15.31
2023-09-21 12:00:00+00:00 15.34
2023-09-21 12:30:00+00:00 15.44
2023-09-21 13:00:00+00:00 15.41
2023-09-21 13:30:00+00:00 15.46
2023-09-21 14:00:00+00:00 15.51
2023-09-21 14:30:00+00:00 15.51
2023-09-21 15:00:00+00:00 15.50
2023-09-22 06:00:00+00:00 15.59
2023-09-22 06:30:00+00:00 15.52
2023-09-22 07:00:00+00:00 15.45
2023-09-22 07:30:00+00:00 15.49
2023-09-22 08:00:00+00:00 15.47
2023-09-22 08:30:00+00:00 15.44
2023-09-22 09:00:00+00:00 15.44
2023-09-22 09:30:00+00:00 15.46
2023-09-22 10:00:00+00:00 15.50
2023-09-22 10:30:00+00:00 15.45
2023-09-22 11:00:00+00:00 15.36
2023-09-22 11:30:00+00:00 15.32
2023-09-22 12:00:00+00:00 15.32
2023-09-22 12:30:00+00:00 15.38
2023-09-22 13:00:00+00:00 15.32
2023-09-22 13:30:00+00:00 15.24
2023-09-22 14:00:00+00:00 15.20
2023-09-22 14:30:00+00:00 15.20
2023-09-22 15:00:00+00:00 15.23
Mean of the first 10 values: price 15.467
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 15.32
2023-09-25 10:00:00+03:00 15.44
2023-09-25 11:00:00+03:00 15.45
2023-09-25 12:00:00+03:00 15.47
2023-09-25 13:00:00+03:00 15.67
2023-09-25 14:00:00+03:00 15.47
2023-09-25 15:00:00+03:00 15.41
2023-09-25 16:00:00+03:00 15.50
2023-09-25 17:00:00+03:00 15.49
2023-09-25 18:00:00+03:00 15.45
price
2018-01-02 10:00:00+03:00 3.219000e-01
2018-01-02 11:00:00+03:00 -1.950000e-02
2018-01-02 12:00:00+03:00 -9.760000e-02
2018-01-02 13:00:00+03:00 1.950000e-02
2018-01-02 14:00:00+03:00 5.860000e-02
... ...
2023-09-22 13:00:00+00:00 -6.000011e-02
2023-09-22 13:30:00+00:00 -8.000023e-02
2023-09-22 14:00:00+00:00 -3.999977e-02
2023-09-22 14:30:00+00:00 -1.907349e-07
2023-09-22 15:00:00+00:00 3.000019e-02
[17889 rows x 1 columns]
ADF Statistic: -19.19262223125624
p-value: 0.0
Critical Values: {'1%': -3.4307164205666516, '5%': -2.861701943937355, '10%': -2.5668561985195675}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 3, 1) Log Likelihood 20270.570
Date: Sun, 24 Dec 2023 AIC -40477.139
Time: 22:34:03 BIC -40227.800
Sample: 0 HQIC -40395.120
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.0194 0.004 -256.005 0.000 -1.027 -1.012
ar.L2 -0.9272 0.007 -136.216 0.000 -0.940 -0.914
ar.L3 -0.9346 0.009 -108.890 0.000 -0.951 -0.918
ar.L4 -0.9026 0.011 -83.117 0.000 -0.924 -0.881
ar.L5 -0.9102 0.013 -72.630 0.000 -0.935 -0.886
ar.L6 -0.8482 0.014 -60.362 0.000 -0.876 -0.821
ar.L7 -0.8666 0.015 -56.563 0.000 -0.897 -0.837
ar.L8 -0.8212 0.016 -50.451 0.000 -0.853 -0.789
ar.L9 -0.8293 0.017 -48.850 0.000 -0.863 -0.796
ar.L10 -0.7765 0.018 -43.796 0.000 -0.811 -0.742
ar.L11 -0.7690 0.019 -41.494 0.000 -0.805 -0.733
ar.L12 -0.7222 0.019 -38.381 0.000 -0.759 -0.685
ar.L13 -0.7280 0.019 -37.461 0.000 -0.766 -0.690
ar.L14 -0.6596 0.020 -33.337 0.000 -0.698 -0.621
ar.L15 -0.6472 0.020 -32.758 0.000 -0.686 -0.608
ar.L16 -0.5979 0.019 -31.036 0.000 -0.636 -0.560
ar.L17 -0.5934 0.019 -31.331 0.000 -0.631 -0.556
ar.L18 -0.5185 0.019 -27.382 0.000 -0.556 -0.481
ar.L19 -0.4771 0.018 -26.287 0.000 -0.513 -0.442
ar.L20 -0.3913 0.018 -22.296 0.000 -0.426 -0.357
ar.L21 -0.3865 0.017 -23.269 0.000 -0.419 -0.354
ar.L22 -0.3226 0.016 -20.801 0.000 -0.353 -0.292
ar.L23 -0.3157 0.015 -21.324 0.000 -0.345 -0.287
ar.L24 -0.2377 0.014 -17.254 0.000 -0.265 -0.211
ar.L25 -0.2236 0.013 -17.254 0.000 -0.249 -0.198
ar.L26 -0.1693 0.011 -14.751 0.000 -0.192 -0.147
ar.L27 -0.1594 0.011 -14.765 0.000 -0.181 -0.138
ar.L28 -0.0733 0.010 -7.554 0.000 -0.092 -0.054
ar.L29 -0.0819 0.008 -10.861 0.000 -0.097 -0.067
ar.L30 0.0201 0.006 3.519 0.000 0.009 0.031
ma.L1 -0.9516 0.003 -365.474 0.000 -0.957 -0.946
sigma2 0.0060 1.32e-05 457.634 0.000 0.006 0.006
===================================================================================
Ljung-Box (L1) (Q): 0.12 Jarque-Bera (JB): 2825209.23
Prob(Q): 0.73 Prob(JB): 0.00
Heteroskedasticity (H): 3.67 Skew: 0.42
Prob(H) (two-sided): 0.00 Kurtosis: 64.56
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 15.250316
17891 15.198568
17892 15.209644
17893 15.201208
17894 15.194959
17895 15.195164
17896 15.178850
17897 15.167375
17898 15.161806
17899 15.145719
Name: predicted_mean, dtype: float64
lower price upper price
17890 15.098100 15.402532
17891 14.980153 15.416984
17892 14.927905 15.491383
17893 14.864602 15.537815
17894 14.804309 15.585609
17895 14.754110 15.636219
17896 14.684471 15.673228
17897 14.622806 15.711944
17898 14.564311 15.759300
17899 14.497152 15.794285
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.29304138553825776 Weighted Mean Absolute Percentage Error (WMAPE): 1.7885766417985445
forecastplusyahoo('ISCTR', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 3.1299 ISCTR
2021-12-27 10:00:00+03:00 3.1702 ISCTR
2021-12-27 11:00:00+03:00 3.1823 ISCTR
2021-12-27 12:00:00+03:00 3.1984 ISCTR
2021-12-27 13:00:00+03:00 3.1864 ISCTR
... ... ...
2023-09-22 14:00:00+03:00 24.2400 ISCTR
2023-09-22 15:00:00+03:00 24.0800 ISCTR
2023-09-22 16:00:00+03:00 24.7800 ISCTR
2023-09-22 17:00:00+03:00 24.9600 ISCTR
2023-09-22 18:00:00+03:00 24.9000 ISCTR
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for ISCTR.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 3.129900
2021-12-27 06:30:00+00:00 3.510071
2021-12-27 07:00:00+00:00 3.170200
2021-12-27 07:30:00+00:00 3.541572
2021-12-27 08:00:00+00:00 3.182300
2021-12-27 08:30:00+00:00 3.564072
2021-12-27 09:00:00+00:00 3.198400
2021-12-27 09:30:00+00:00 3.546072
2021-12-27 10:00:00+00:00 3.186400
2021-12-27 10:30:00+00:00 3.559572
2021-12-27 11:00:00+00:00 3.162100
2021-12-27 11:30:00+00:00 3.537072
2021-12-27 12:00:00+00:00 3.182300
2021-12-27 12:30:00+00:00 3.541572
2021-12-27 13:00:00+00:00 3.190400
2021-12-27 13:30:00+00:00 3.577573
2021-12-27 14:00:00+00:00 3.214600
2021-12-27 15:00:00+00:00 3.214600
2021-12-28 06:00:00+00:00 3.194300
2021-12-28 06:30:00+00:00 3.469570
2021-12-28 07:00:00+00:00 3.105600
2021-12-28 07:30:00+00:00 3.460570
2021-12-28 08:00:00+00:00 3.081500
2021-12-28 08:30:00+00:00 3.429070
2021-12-28 09:00:00+00:00 3.073400
2021-12-28 09:30:00+00:00 3.429070
2021-12-28 10:00:00+00:00 3.077400
2021-12-28 10:30:00+00:00 3.438070
2021-12-28 11:00:00+00:00 3.065300
2021-12-28 11:30:00+00:00 3.429070
2021-12-28 12:00:00+00:00 3.073400
2021-12-28 12:30:00+00:00 3.420069
2021-12-28 13:00:00+00:00 3.049200
2021-12-28 13:30:00+00:00 3.366068
2021-12-28 14:00:00+00:00 3.008900
2021-12-28 15:00:00+00:00 3.008900
2021-12-29 06:00:00+00:00 2.976600
2021-12-29 06:30:00+00:00 3.294067
2021-12-29 07:00:00+00:00 2.948400
2021-12-29 07:30:00+00:00 3.334568
2021-12-29 08:00:00+00:00 3.000800
2021-12-29 08:30:00+00:00 3.352568
2021-12-29 09:00:00+00:00 2.996800
2021-12-29 09:30:00+00:00 3.334568
2021-12-29 10:00:00+00:00 3.000800
2021-12-29 10:30:00+00:00 3.343568
2021-12-29 11:00:00+00:00 3.004900
2021-12-29 11:30:00+00:00 3.384069
2021-12-29 12:00:00+00:00 3.025000
2021-12-29 12:30:00+00:00 3.411069
price
2023-09-21 10:00:00+00:00 22.960000
2023-09-21 10:30:00+00:00 22.440001
2023-09-21 11:00:00+00:00 22.660000
2023-09-21 11:30:00+00:00 22.620001
2023-09-21 12:00:00+00:00 22.720000
2023-09-21 12:30:00+00:00 23.020000
2023-09-21 13:00:00+00:00 22.980000
2023-09-21 13:30:00+00:00 22.940001
2023-09-21 14:00:00+00:00 23.000000
2023-09-21 14:30:00+00:00 23.000000
2023-09-21 15:00:00+00:00 23.140000
2023-09-22 06:00:00+00:00 23.180000
2023-09-22 06:30:00+00:00 23.080000
2023-09-22 07:00:00+00:00 23.160000
2023-09-22 07:30:00+00:00 23.540001
2023-09-22 08:00:00+00:00 23.580000
2023-09-22 08:30:00+00:00 23.540001
2023-09-22 09:00:00+00:00 23.480000
2023-09-22 09:30:00+00:00 23.799999
2023-09-22 10:00:00+00:00 24.400000
2023-09-22 10:30:00+00:00 24.480000
2023-09-22 11:00:00+00:00 24.240000
2023-09-22 11:30:00+00:00 24.100000
2023-09-22 12:00:00+00:00 24.080000
2023-09-22 12:30:00+00:00 24.920000
2023-09-22 13:00:00+00:00 24.780000
2023-09-22 13:30:00+00:00 25.040001
2023-09-22 14:00:00+00:00 24.960000
2023-09-22 14:30:00+00:00 24.959999
2023-09-22 15:00:00+00:00 24.900000
Mean of the first 10 values: price 25.218
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 25.04
2023-09-25 10:00:00+03:00 25.04
2023-09-25 11:00:00+03:00 25.28
2023-09-25 12:00:00+03:00 25.20
2023-09-25 13:00:00+03:00 25.24
2023-09-25 14:00:00+03:00 25.40
2023-09-25 15:00:00+03:00 25.32
2023-09-25 16:00:00+03:00 25.28
2023-09-25 17:00:00+03:00 25.22
2023-09-25 18:00:00+03:00 25.16
price
2018-01-02 10:00:00+03:00 1.130000e-02
2018-01-02 11:00:00+03:00 0.000000e+00
2018-01-02 12:00:00+03:00 -7.500000e-03
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 3.000000e-02
... ...
2023-09-22 13:00:00+00:00 -1.400001e-01
2023-09-22 13:30:00+00:00 2.600009e-01
2023-09-22 14:00:00+00:00 -8.000092e-02
2023-09-22 14:30:00+00:00 -9.155273e-07
2023-09-22 15:00:00+00:00 -5.999908e-02
[17890 rows x 1 columns]
ADF Statistic: -17.421209351684237
p-value: 4.824718674506475e-30
Critical Values: {'1%': -3.4307164410996687, '5%': -2.8617019530116066, '10%': -2.5668562033496514}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17891
Model: ARIMA(30, 1, 1) Log Likelihood 12847.278
Date: Sun, 24 Dec 2023 AIC -25630.557
Time: 22:36:52 BIC -25381.213
Sample: 0 HQIC -25548.537
- 17891
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.4334 0.104 -4.173 0.000 -0.637 -0.230
ar.L2 0.2290 0.075 3.041 0.002 0.081 0.377
ar.L3 -0.0700 0.005 -14.434 0.000 -0.080 -0.061
ar.L4 -0.0051 0.008 -0.601 0.548 -0.022 0.011
ar.L5 -0.0313 0.005 -5.843 0.000 -0.042 -0.021
ar.L6 0.0515 0.006 7.977 0.000 0.039 0.064
ar.L7 -0.0235 0.007 -3.178 0.001 -0.038 -0.009
ar.L8 0.0228 0.006 3.525 0.000 0.010 0.035
ar.L9 -0.0222 0.007 -3.387 0.001 -0.035 -0.009
ar.L10 0.0406 0.006 6.579 0.000 0.028 0.053
ar.L11 0.0044 0.007 0.648 0.517 -0.009 0.018
ar.L12 -0.0174 0.006 -2.728 0.006 -0.030 -0.005
ar.L13 0.0020 0.005 0.361 0.718 -0.009 0.013
ar.L14 -0.0103 0.005 -2.151 0.031 -0.020 -0.001
ar.L15 0.0144 0.005 2.958 0.003 0.005 0.024
ar.L16 -0.1499 0.004 -38.343 0.000 -0.158 -0.142
ar.L17 -0.0405 0.015 -2.633 0.008 -0.071 -0.010
ar.L18 0.3513 0.009 39.244 0.000 0.334 0.369
ar.L19 0.2959 0.034 8.711 0.000 0.229 0.363
ar.L20 -0.2254 0.041 -5.539 0.000 -0.305 -0.146
ar.L21 -0.0543 0.012 -4.448 0.000 -0.078 -0.030
ar.L22 0.0541 0.010 5.437 0.000 0.035 0.074
ar.L23 -0.0477 0.005 -9.074 0.000 -0.058 -0.037
ar.L24 0.0085 0.006 1.308 0.191 -0.004 0.021
ar.L25 -0.0264 0.005 -4.990 0.000 -0.037 -0.016
ar.L26 0.0404 0.007 6.146 0.000 0.027 0.053
ar.L27 -0.0210 0.007 -2.878 0.004 -0.035 -0.007
ar.L28 0.0227 0.006 3.601 0.000 0.010 0.035
ar.L29 -0.0418 0.006 -6.715 0.000 -0.054 -0.030
ar.L30 0.0631 0.007 8.899 0.000 0.049 0.077
ma.L1 -0.2904 0.104 -2.793 0.005 -0.494 -0.087
sigma2 0.0139 5.32e-05 261.900 0.000 0.014 0.014
===================================================================================
Ljung-Box (L1) (Q): 0.09 Jarque-Bera (JB): 354289.18
Prob(Q): 0.77 Prob(JB): 0.00
Heteroskedasticity (H): 77.80 Skew: -0.09
Prob(H) (two-sided): 0.00 Kurtosis: 24.80
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891 24.929343
17892 24.777142
17893 25.082632
17894 25.000581
17895 25.025407
17896 24.824054
17897 25.010176
17898 25.197724
17899 25.349444
17900 25.096077
Name: predicted_mean, dtype: float64
lower price upper price
17891 24.698095 25.160590
17892 24.537235 25.017048
17893 24.776988 25.388276
17894 24.684525 25.316637
17895 24.667965 25.382849
17896 24.455618 25.192490
17897 24.605687 25.414665
17898 24.782900 25.612548
17899 24.902514 25.796375
17900 24.639836 25.552319
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.25718235592753785 Weighted Mean Absolute Percentage Error (WMAPE): 0.9003640330858422
forecastplusyahoo('VAKBN', 30, 1, 3)
price short_name
timestamp
2021-12-27 09:00:00+03:00 3.86 VAKBN
2021-12-27 10:00:00+03:00 3.87 VAKBN
2021-12-27 11:00:00+03:00 3.86 VAKBN
2021-12-27 12:00:00+03:00 3.86 VAKBN
2021-12-27 13:00:00+03:00 3.85 VAKBN
... ... ...
2023-09-22 14:00:00+03:00 13.58 VAKBN
2023-09-22 15:00:00+03:00 13.53 VAKBN
2023-09-22 16:00:00+03:00 13.56 VAKBN
2023-09-22 17:00:00+03:00 13.49 VAKBN
2023-09-22 18:00:00+03:00 13.50 VAKBN
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for VAKBN.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 3.86
2021-12-27 06:30:00+00:00 3.85
2021-12-27 07:00:00+00:00 3.87
2021-12-27 07:30:00+00:00 3.86
2021-12-27 08:00:00+00:00 3.86
2021-12-27 08:30:00+00:00 3.86
2021-12-27 09:00:00+00:00 3.86
2021-12-27 09:30:00+00:00 3.84
2021-12-27 10:00:00+00:00 3.85
2021-12-27 10:30:00+00:00 3.85
2021-12-27 11:00:00+00:00 3.83
2021-12-27 11:30:00+00:00 3.83
2021-12-27 12:00:00+00:00 3.84
2021-12-27 12:30:00+00:00 3.84
2021-12-27 13:00:00+00:00 3.86
2021-12-27 13:30:00+00:00 3.85
2021-12-27 14:00:00+00:00 3.83
2021-12-27 15:00:00+00:00 3.83
2021-12-28 06:00:00+00:00 3.84
2021-12-28 06:30:00+00:00 3.79
2021-12-28 07:00:00+00:00 3.78
2021-12-28 07:30:00+00:00 3.79
2021-12-28 08:00:00+00:00 3.78
2021-12-28 08:30:00+00:00 3.77
2021-12-28 09:00:00+00:00 3.77
2021-12-28 09:30:00+00:00 3.76
2021-12-28 10:00:00+00:00 3.77
2021-12-28 10:30:00+00:00 3.77
2021-12-28 11:00:00+00:00 3.77
2021-12-28 11:30:00+00:00 3.77
2021-12-28 12:00:00+00:00 3.78
2021-12-28 12:30:00+00:00 3.76
2021-12-28 13:00:00+00:00 3.76
2021-12-28 13:30:00+00:00 3.68
2021-12-28 14:00:00+00:00 3.68
2021-12-28 15:00:00+00:00 3.69
2021-12-29 06:00:00+00:00 3.65
2021-12-29 06:30:00+00:00 3.65
2021-12-29 07:00:00+00:00 3.66
2021-12-29 07:30:00+00:00 3.73
2021-12-29 08:00:00+00:00 3.74
2021-12-29 08:30:00+00:00 3.72
2021-12-29 09:00:00+00:00 3.73
2021-12-29 09:30:00+00:00 3.73
2021-12-29 10:00:00+00:00 3.73
2021-12-29 10:30:00+00:00 3.72
2021-12-29 11:00:00+00:00 3.74
2021-12-29 11:30:00+00:00 3.74
2021-12-29 12:00:00+00:00 3.75
2021-12-29 12:30:00+00:00 3.75
price
2023-09-21 10:00:00+00:00 13.69
2023-09-21 10:30:00+00:00 13.47
2023-09-21 11:00:00+00:00 13.52
2023-09-21 11:30:00+00:00 13.49
2023-09-21 12:00:00+00:00 13.49
2023-09-21 12:30:00+00:00 13.66
2023-09-21 13:00:00+00:00 13.59
2023-09-21 13:30:00+00:00 13.61
2023-09-21 14:00:00+00:00 13.63
2023-09-21 14:30:00+00:00 13.63
2023-09-21 15:00:00+00:00 13.64
2023-09-22 06:00:00+00:00 13.65
2023-09-22 06:30:00+00:00 13.69
2023-09-22 07:00:00+00:00 13.69
2023-09-22 07:30:00+00:00 13.76
2023-09-22 08:00:00+00:00 13.75
2023-09-22 08:30:00+00:00 13.71
2023-09-22 09:00:00+00:00 13.70
2023-09-22 09:30:00+00:00 13.73
2023-09-22 10:00:00+00:00 13.79
2023-09-22 10:30:00+00:00 13.70
2023-09-22 11:00:00+00:00 13.58
2023-09-22 11:30:00+00:00 13.52
2023-09-22 12:00:00+00:00 13.53
2023-09-22 12:30:00+00:00 13.63
2023-09-22 13:00:00+00:00 13.56
2023-09-22 13:30:00+00:00 13.49
2023-09-22 14:00:00+00:00 13.49
2023-09-22 14:30:00+00:00 13.49
2023-09-22 15:00:00+00:00 13.50
Mean of the first 10 values: price 13.799
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 13.59
2023-09-25 10:00:00+03:00 13.64
2023-09-25 11:00:00+03:00 13.72
2023-09-25 12:00:00+03:00 13.85
2023-09-25 13:00:00+03:00 13.93
2023-09-25 14:00:00+03:00 13.85
2023-09-25 15:00:00+03:00 13.80
2023-09-25 16:00:00+03:00 13.86
2023-09-25 17:00:00+03:00 13.85
2023-09-25 18:00:00+03:00 13.90
price
2018-01-02 10:00:00+03:00 1.085000e-01
2018-01-02 11:00:00+03:00 7.880000e-02
2018-01-02 12:00:00+03:00 0.000000e+00
2018-01-02 13:00:00+03:00 9.900000e-03
2018-01-02 14:00:00+03:00 3.940000e-02
... ...
2023-09-22 13:00:00+00:00 -7.000011e-02
2023-09-22 13:30:00+00:00 -7.000023e-02
2023-09-22 14:00:00+00:00 2.288818e-07
2023-09-22 14:30:00+00:00 -2.288818e-07
2023-09-22 15:00:00+00:00 1.000023e-02
[17889 rows x 1 columns]
ADF Statistic: -18.14892823532758
p-value: 2.4790136776995764e-30
Critical Values: {'1%': -3.4307164410996687, '5%': -2.8617019530116066, '10%': -2.5668562033496514}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 3, 1) Log Likelihood 23168.626
Date: Sun, 24 Dec 2023 AIC -46273.252
Time: 22:47:21 BIC -46023.913
Sample: 0 HQIC -46191.233
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.0633 0.004 -287.864 0.000 -1.071 -1.056
ar.L2 -0.9828 0.006 -172.024 0.000 -0.994 -0.972
ar.L3 -0.9560 0.007 -128.643 0.000 -0.971 -0.941
ar.L4 -0.8979 0.009 -100.018 0.000 -0.915 -0.880
ar.L5 -0.8795 0.010 -86.046 0.000 -0.900 -0.859
ar.L6 -0.8215 0.011 -72.137 0.000 -0.844 -0.799
ar.L7 -0.8304 0.012 -67.613 0.000 -0.854 -0.806
ar.L8 -0.7807 0.013 -58.841 0.000 -0.807 -0.755
ar.L9 -0.7890 0.013 -58.569 0.000 -0.815 -0.763
ar.L10 -0.7314 0.014 -52.916 0.000 -0.759 -0.704
ar.L11 -0.7273 0.014 -50.358 0.000 -0.756 -0.699
ar.L12 -0.6694 0.015 -45.229 0.000 -0.698 -0.640
ar.L13 -0.6932 0.014 -48.770 0.000 -0.721 -0.665
ar.L14 -0.6221 0.014 -44.261 0.000 -0.650 -0.595
ar.L15 -0.6113 0.014 -44.457 0.000 -0.638 -0.584
ar.L16 -0.5564 0.014 -40.783 0.000 -0.583 -0.530
ar.L17 -0.5728 0.013 -43.196 0.000 -0.599 -0.547
ar.L18 -0.4914 0.013 -37.028 0.000 -0.517 -0.465
ar.L19 -0.4182 0.013 -32.198 0.000 -0.444 -0.393
ar.L20 -0.3467 0.013 -25.815 0.000 -0.373 -0.320
ar.L21 -0.3779 0.014 -27.805 0.000 -0.405 -0.351
ar.L22 -0.3141 0.014 -23.090 0.000 -0.341 -0.287
ar.L23 -0.3222 0.014 -23.135 0.000 -0.350 -0.295
ar.L24 -0.2502 0.013 -19.373 0.000 -0.275 -0.225
ar.L25 -0.2252 0.012 -18.158 0.000 -0.249 -0.201
ar.L26 -0.1578 0.011 -14.050 0.000 -0.180 -0.136
ar.L27 -0.1595 0.010 -15.589 0.000 -0.180 -0.139
ar.L28 -0.0875 0.009 -9.861 0.000 -0.105 -0.070
ar.L29 -0.0836 0.007 -11.955 0.000 -0.097 -0.070
ar.L30 -0.0017 0.005 -0.345 0.730 -0.011 0.008
ma.L1 -0.9772 0.002 -562.780 0.000 -0.981 -0.974
sigma2 0.0044 9.97e-06 438.595 0.000 0.004 0.004
===================================================================================
Ljung-Box (L1) (Q): 0.02 Jarque-Bera (JB): 4231751.69
Prob(Q): 0.89 Prob(JB): 0.00
Heteroskedasticity (H): 6.42 Skew: 0.45
Prob(H) (two-sided): 0.00 Kurtosis: 78.35
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 13.504667
17891 13.485549
17892 13.486290
17893 13.496010
17894 13.493438
17895 13.493881
17896 13.485630
17897 13.482546
17898 13.491759
17899 13.477176
Name: predicted_mean, dtype: float64
lower price upper price
17890 13.375033 13.634301
17891 13.305888 13.665210
17892 13.259670 13.712909
17893 13.227902 13.764119
17894 13.184049 13.802827
17895 13.145791 13.841972
17896 13.097622 13.873638
17897 13.057545 13.907546
17898 13.028089 13.955429
17899 12.977063 13.977289
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.3281600088901808 Weighted Mean Absolute Percentage Error (WMAPE): 2.241505848139771
forecastplusyahoo('VESTL', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 24.94 VESTL
2021-12-27 10:00:00+03:00 25.44 VESTL
2021-12-27 11:00:00+03:00 25.16 VESTL
2021-12-27 12:00:00+03:00 24.98 VESTL
2021-12-27 13:00:00+03:00 25.08 VESTL
... ... ...
2023-09-22 14:00:00+03:00 63.10 VESTL
2023-09-22 15:00:00+03:00 62.55 VESTL
2023-09-22 16:00:00+03:00 64.15 VESTL
2023-09-22 17:00:00+03:00 64.20 VESTL
2023-09-22 18:00:00+03:00 64.10 VESTL
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for VESTL.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 24.940000
2021-12-27 06:30:00+00:00 25.379999
2021-12-27 07:00:00+00:00 25.440000
2021-12-27 07:30:00+00:00 25.139999
2021-12-27 08:00:00+00:00 25.160000
2021-12-27 08:30:00+00:00 25.080000
2021-12-27 09:00:00+00:00 24.980000
2021-12-27 09:30:00+00:00 24.900000
2021-12-27 10:00:00+00:00 25.080000
2021-12-27 10:30:00+00:00 25.020000
2021-12-27 11:00:00+00:00 24.680000
2021-12-27 11:30:00+00:00 24.540001
2021-12-27 12:00:00+00:00 24.620000
2021-12-27 12:30:00+00:00 24.620001
2021-12-27 13:00:00+00:00 24.700000
2021-12-27 13:30:00+00:00 24.860001
2021-12-27 14:00:00+00:00 24.720000
2021-12-27 15:00:00+00:00 24.680000
2021-12-28 06:00:00+00:00 24.840000
2021-12-28 06:30:00+00:00 24.660000
2021-12-28 07:00:00+00:00 24.740000
2021-12-28 07:30:00+00:00 24.680000
2021-12-28 08:00:00+00:00 24.500000
2021-12-28 08:30:00+00:00 24.459999
2021-12-28 09:00:00+00:00 24.420000
2021-12-28 09:30:00+00:00 24.420000
2021-12-28 10:00:00+00:00 24.320000
2021-12-28 10:30:00+00:00 24.320000
2021-12-28 11:00:00+00:00 24.260000
2021-12-28 11:30:00+00:00 24.320000
2021-12-28 12:00:00+00:00 24.260000
2021-12-28 12:30:00+00:00 24.260000
2021-12-28 13:00:00+00:00 24.100000
2021-12-28 13:30:00+00:00 23.900000
2021-12-28 14:00:00+00:00 23.840000
2021-12-28 15:00:00+00:00 23.800000
2021-12-29 06:00:00+00:00 23.880000
2021-12-29 06:30:00+00:00 23.660000
2021-12-29 07:00:00+00:00 23.660000
2021-12-29 07:30:00+00:00 24.059999
2021-12-29 08:00:00+00:00 24.080000
2021-12-29 08:30:00+00:00 24.100000
2021-12-29 09:00:00+00:00 24.160000
2021-12-29 09:30:00+00:00 24.139999
2021-12-29 10:00:00+00:00 24.160000
2021-12-29 10:30:00+00:00 24.120001
2021-12-29 11:00:00+00:00 24.160000
2021-12-29 11:30:00+00:00 24.299999
2021-12-29 12:00:00+00:00 24.320000
2021-12-29 12:30:00+00:00 24.379999
price
2023-09-21 10:00:00+00:00 59.350000
2023-09-21 10:30:00+00:00 60.099998
2023-09-21 11:00:00+00:00 60.350000
2023-09-21 11:30:00+00:00 60.250000
2023-09-21 12:00:00+00:00 60.050000
2023-09-21 12:30:00+00:00 60.549999
2023-09-21 13:00:00+00:00 60.950000
2023-09-21 13:30:00+00:00 61.250000
2023-09-21 14:00:00+00:00 61.700000
2023-09-21 14:30:00+00:00 61.700001
2023-09-21 15:00:00+00:00 61.900000
2023-09-22 06:00:00+00:00 62.050000
2023-09-22 06:30:00+00:00 62.950001
2023-09-22 07:00:00+00:00 62.550000
2023-09-22 07:30:00+00:00 62.799999
2023-09-22 08:00:00+00:00 63.400000
2023-09-22 08:30:00+00:00 63.150002
2023-09-22 09:00:00+00:00 63.100000
2023-09-22 09:30:00+00:00 62.650002
2023-09-22 10:00:00+00:00 63.100000
2023-09-22 10:30:00+00:00 63.250000
2023-09-22 11:00:00+00:00 63.100000
2023-09-22 11:30:00+00:00 62.599998
2023-09-22 12:00:00+00:00 62.550000
2023-09-22 12:30:00+00:00 63.299999
2023-09-22 13:00:00+00:00 64.150000
2023-09-22 13:30:00+00:00 64.050003
2023-09-22 14:00:00+00:00 64.200000
2023-09-22 14:30:00+00:00 64.199997
2023-09-22 15:00:00+00:00 64.100000
Mean of the first 10 values: price 64.705
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 64.40
2023-09-25 10:00:00+03:00 65.10
2023-09-25 11:00:00+03:00 64.80
2023-09-25 12:00:00+03:00 64.75
2023-09-25 13:00:00+03:00 65.00
2023-09-25 14:00:00+03:00 64.45
2023-09-25 15:00:00+03:00 64.30
2023-09-25 16:00:00+03:00 64.70
2023-09-25 17:00:00+03:00 64.80
2023-09-25 18:00:00+03:00 64.75
price
2018-01-02 10:00:00+03:00 0.173300
2018-01-02 11:00:00+03:00 0.315000
2018-01-02 12:00:00+03:00 0.039300
2018-01-02 13:00:00+03:00 0.000000
2018-01-02 14:00:00+03:00 0.322900
... ...
2023-09-22 13:00:00+00:00 0.850001
2023-09-22 13:30:00+00:00 -0.099997
2023-09-22 14:00:00+00:00 0.149997
2023-09-22 14:30:00+00:00 -0.000003
2023-09-22 15:00:00+00:00 -0.099997
[17889 rows x 1 columns]
ADF Statistic: -26.126464966641958
p-value: 0.0
Critical Values: {'1%': -3.430716174349812, '5%': -2.8617018351255985, '10%': -2.566856140600752}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17890
Model: ARIMA(30, 1, 1) Log Likelihood -2459.988
Date: Sun, 24 Dec 2023 AIC 4983.975
Time: 22:38:08 BIC 5233.317
Sample: 0 HQIC 5065.995
- 17890
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0023 8.536 0.000 1.000 -16.728 16.732
ar.L2 -0.0468 0.043 -1.092 0.275 -0.131 0.037
ar.L3 0.0068 0.399 0.017 0.986 -0.776 0.790
ar.L4 -0.0009 0.059 -0.015 0.988 -0.117 0.116
ar.L5 0.0101 0.009 1.131 0.258 -0.007 0.028
ar.L6 0.0111 0.086 0.128 0.898 -0.158 0.181
ar.L7 0.0013 0.095 0.014 0.989 -0.184 0.187
ar.L8 0.0096 0.011 0.864 0.388 -0.012 0.032
ar.L9 0.0286 0.082 0.347 0.729 -0.133 0.190
ar.L10 -0.0062 0.244 -0.025 0.980 -0.484 0.471
ar.L11 0.0128 0.053 0.240 0.810 -0.092 0.117
ar.L12 -0.0293 0.110 -0.267 0.789 -0.244 0.185
ar.L13 0.0343 0.250 0.137 0.891 -0.455 0.524
ar.L14 0.0218 0.294 0.074 0.941 -0.554 0.598
ar.L15 0.0145 0.185 0.078 0.938 -0.349 0.378
ar.L16 -0.0141 0.123 -0.115 0.909 -0.255 0.227
ar.L17 0.0053 0.121 0.044 0.965 -0.231 0.242
ar.L18 0.0158 0.045 0.349 0.727 -0.073 0.105
ar.L19 0.0054 0.135 0.040 0.968 -0.259 0.270
ar.L20 -0.0304 0.046 -0.661 0.509 -0.121 0.060
ar.L21 0.0080 0.260 0.031 0.975 -0.501 0.517
ar.L22 -0.0013 0.070 -0.018 0.985 -0.138 0.135
ar.L23 -0.0046 0.011 -0.400 0.689 -0.027 0.018
ar.L24 -0.0203 0.039 -0.518 0.604 -0.097 0.057
ar.L25 -0.0122 0.173 -0.070 0.944 -0.352 0.327
ar.L26 -0.0098 0.104 -0.094 0.925 -0.214 0.194
ar.L27 -0.0100 0.083 -0.120 0.904 -0.173 0.153
ar.L28 -0.0139 0.085 -0.163 0.871 -0.181 0.153
ar.L29 0.0086 0.119 0.072 0.942 -0.224 0.241
ar.L30 -0.0005 0.074 -0.007 0.995 -0.145 0.144
ma.L1 0.0028 8.536 0.000 1.000 -16.728 16.733
sigma2 0.0771 0.000 380.802 0.000 0.077 0.077
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 2712737.08
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 24.03 Skew: 1.49
Prob(H) (two-sided): 0.00 Kurtosis: 63.25
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890 64.120903
17891 64.065305
17892 64.034669
17893 64.082778
17894 64.100479
17895 64.041660
17896 64.000511
17897 63.994282
17898 64.051274
17899 64.034090
Name: predicted_mean, dtype: float64
lower price upper price
17890 63.576747 64.665059
17891 63.293820 64.836790
17892 63.103492 64.965845
17893 63.013813 65.151742
17894 62.909247 65.291710
17895 62.737494 65.345825
17896 62.590140 65.410881
17897 62.485075 65.503490
17898 62.447714 65.654834
17899 62.336365 65.731815
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.6939838468610346 Weighted Mean Absolute Percentage Error (WMAPE): 1.0082760508349327
forecastplusyahoo('YKBNK', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 3.1711 YKBNK
2021-12-27 10:00:00+03:00 3.2338 YKBNK
2021-12-27 11:00:00+03:00 3.2517 YKBNK
2021-12-27 12:00:00+03:00 3.2517 YKBNK
2021-12-27 13:00:00+03:00 3.2517 YKBNK
... ... ...
2023-09-22 14:00:00+03:00 16.9100 YKBNK
2023-09-22 15:00:00+03:00 16.9300 YKBNK
2023-09-22 16:00:00+03:00 16.9900 YKBNK
2023-09-22 17:00:00+03:00 16.8100 YKBNK
2023-09-22 18:00:00+03:00 16.8000 YKBNK
[4324 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
Stock data for YKBNK.IS:
Open High Low Close \
Datetime
2023-09-25 09:30:00+03:00 138.000000 140.199997 138.000000 139.300003
2023-09-25 10:30:00+03:00 139.399994 140.100006 139.100006 140.100006
2023-09-25 11:30:00+03:00 140.000000 141.100006 139.399994 140.899994
2023-09-25 12:30:00+03:00 140.899994 141.899994 140.600006 141.899994
2023-09-25 13:30:00+03:00 141.899994 142.699997 141.300003 142.500000
2023-09-25 14:30:00+03:00 142.500000 143.199997 141.800003 142.100006
2023-09-25 15:30:00+03:00 142.100006 143.100006 141.899994 143.000000
2023-09-25 16:30:00+03:00 143.000000 143.500000 142.199997 142.800003
2023-09-25 17:30:00+03:00 142.899994 143.000000 142.100006 142.300003
Adj Close Volume
Datetime
2023-09-25 09:30:00+03:00 139.300003 0
2023-09-25 10:30:00+03:00 140.100006 1997811
2023-09-25 11:30:00+03:00 140.899994 2334550
2023-09-25 12:30:00+03:00 141.899994 2153753
2023-09-25 13:30:00+03:00 142.500000 1863498
2023-09-25 14:30:00+03:00 142.100006 2693308
2023-09-25 15:30:00+03:00 143.000000 1611697
2023-09-25 16:30:00+03:00 142.800003 1813080
2023-09-25 17:30:00+03:00 142.300003 1375453
price
2021-12-27 06:00:00+00:00 3.1711
2021-12-27 06:30:00+00:00 3.5600
2021-12-27 07:00:00+00:00 3.2338
2021-12-27 07:30:00+00:00 3.6200
2021-12-27 08:00:00+00:00 3.2517
2021-12-27 08:30:00+00:00 3.6500
2021-12-27 09:00:00+00:00 3.2517
2021-12-27 09:30:00+00:00 3.6200
2021-12-27 10:00:00+00:00 3.2517
2021-12-27 10:30:00+00:00 3.6300
2021-12-27 11:00:00+00:00 3.2249
2021-12-27 11:30:00+00:00 3.5900
2021-12-27 12:00:00+00:00 3.2697
2021-12-27 12:30:00+00:00 3.6600
2021-12-27 13:00:00+00:00 3.2965
2021-12-27 13:30:00+00:00 3.6700
2021-12-27 14:00:00+00:00 3.2786
2021-12-27 15:00:00+00:00 3.2786
2021-12-28 06:00:00+00:00 3.2607
2021-12-28 06:30:00+00:00 3.5300
2021-12-28 07:00:00+00:00 3.1711
2021-12-28 07:30:00+00:00 3.5300
2021-12-28 08:00:00+00:00 3.1532
2021-12-28 08:30:00+00:00 3.5100
2021-12-28 09:00:00+00:00 3.1532
2021-12-28 09:30:00+00:00 3.5100
2021-12-28 10:00:00+00:00 3.1443
2021-12-28 10:30:00+00:00 3.5100
2021-12-28 11:00:00+00:00 3.1532
2021-12-28 11:30:00+00:00 3.5100
2021-12-28 12:00:00+00:00 3.1532
2021-12-28 12:30:00+00:00 3.5100
2021-12-28 13:00:00+00:00 3.1353
2021-12-28 13:30:00+00:00 3.4400
2021-12-28 14:00:00+00:00 3.0816
2021-12-28 15:00:00+00:00 3.0816
2021-12-29 06:00:00+00:00 3.0726
2021-12-29 06:30:00+00:00 3.4100
2021-12-29 07:00:00+00:00 3.0547
2021-12-29 07:30:00+00:00 3.4700
2021-12-29 08:00:00+00:00 3.0994
2021-12-29 08:30:00+00:00 3.4400
2021-12-29 09:00:00+00:00 3.0905
2021-12-29 09:30:00+00:00 3.4500
2021-12-29 10:00:00+00:00 3.0816
2021-12-29 10:30:00+00:00 3.4300
2021-12-29 11:00:00+00:00 3.0905
2021-12-29 11:30:00+00:00 3.4700
2021-12-29 12:00:00+00:00 3.0994
2021-12-29 12:30:00+00:00 3.4800
price
2023-09-21 10:00:00+00:00 16.830000
2023-09-21 10:30:00+00:00 16.559999
2023-09-21 11:00:00+00:00 16.770000
2023-09-21 11:30:00+00:00 16.760000
2023-09-21 12:00:00+00:00 16.850000
2023-09-21 12:30:00+00:00 16.910000
2023-09-21 13:00:00+00:00 16.920000
2023-09-21 13:30:00+00:00 16.850000
2023-09-21 14:00:00+00:00 16.960000
2023-09-21 14:30:00+00:00 16.959999
2023-09-21 15:00:00+00:00 17.000000
2023-09-22 06:00:00+00:00 17.000000
2023-09-22 06:30:00+00:00 16.969999
2023-09-22 07:00:00+00:00 16.900000
2023-09-22 07:30:00+00:00 17.040001
2023-09-22 08:00:00+00:00 17.010000
2023-09-22 08:30:00+00:00 17.000000
2023-09-22 09:00:00+00:00 16.980000
2023-09-22 09:30:00+00:00 17.010000
2023-09-22 10:00:00+00:00 17.060000
2023-09-22 10:30:00+00:00 17.030001
2023-09-22 11:00:00+00:00 16.910000
2023-09-22 11:30:00+00:00 16.879999
2023-09-22 12:00:00+00:00 16.930000
2023-09-22 12:30:00+00:00 17.059999
2023-09-22 13:00:00+00:00 16.990000
2023-09-22 13:30:00+00:00 16.930000
2023-09-22 14:00:00+00:00 16.810000
2023-09-22 14:30:00+00:00 16.809999
2023-09-22 15:00:00+00:00 16.800000
Mean of the first 10 values: price 17.015
dtype: float64
price
timestamp
2023-09-25 09:00:00+03:00 16.89
2023-09-25 10:00:00+03:00 16.90
2023-09-25 11:00:00+03:00 16.91
2023-09-25 12:00:00+03:00 17.02
2023-09-25 13:00:00+03:00 17.09
2023-09-25 14:00:00+03:00 17.02
2023-09-25 15:00:00+03:00 17.02
2023-09-25 16:00:00+03:00 17.12
2023-09-25 17:00:00+03:00 17.08
2023-09-25 18:00:00+03:00 17.10
price
2018-01-02 10:00:00+03:00 1.690000e-02
2018-01-02 11:00:00+03:00 1.690000e-02
2018-01-02 12:00:00+03:00 0.000000e+00
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 1.670000e-02
... ...
2023-09-22 13:00:00+00:00 -6.999947e-02
2023-09-22 13:30:00+00:00 -5.999969e-02
2023-09-22 14:00:00+00:00 -1.200003e-01
2023-09-22 14:30:00+00:00 -5.340576e-07
2023-09-22 15:00:00+00:00 -9.999466e-03
[17888 rows x 1 columns]
ADF Statistic: -20.78216857876311
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 17889
Model: ARIMA(30, 1, 1) Log Likelihood 14652.148
Date: Sun, 24 Dec 2023 AIC -29240.297
Time: 22:41:01 BIC -28990.957
Sample: 0 HQIC -29158.277
- 17889
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.4517 0.145 -3.114 0.002 -0.736 -0.167
ar.L2 0.2383 0.120 1.989 0.047 0.003 0.473
ar.L3 -0.0276 0.011 -2.464 0.014 -0.050 -0.006
ar.L4 0.0131 0.010 1.370 0.171 -0.006 0.032
ar.L5 -0.0342 0.005 -6.442 0.000 -0.045 -0.024
ar.L6 0.0386 0.008 4.998 0.000 0.023 0.054
ar.L7 -0.0127 0.007 -1.785 0.074 -0.027 0.001
ar.L8 0.0415 0.007 6.113 0.000 0.028 0.055
ar.L9 -0.0272 0.009 -2.964 0.003 -0.045 -0.009
ar.L10 0.0251 0.007 3.530 0.000 0.011 0.039
ar.L11 -0.0027 0.007 -0.402 0.688 -0.016 0.010
ar.L12 -0.0361 0.006 -6.083 0.000 -0.048 -0.024
ar.L13 -0.0214 0.007 -3.283 0.001 -0.034 -0.009
ar.L14 -0.0445 0.007 -6.813 0.000 -0.057 -0.032
ar.L15 -0.0120 0.010 -1.257 0.209 -0.031 0.007
ar.L16 -0.1629 0.006 -27.067 0.000 -0.175 -0.151
ar.L17 -0.0395 0.026 -1.528 0.126 -0.090 0.011
ar.L18 0.4268 0.015 27.544 0.000 0.396 0.457
ar.L19 0.3250 0.056 5.783 0.000 0.215 0.435
ar.L20 -0.2698 0.068 -3.962 0.000 -0.403 -0.136
ar.L21 -0.1090 0.014 -7.533 0.000 -0.137 -0.081
ar.L22 0.0075 0.022 0.347 0.729 -0.035 0.050
ar.L23 -0.0508 0.009 -5.814 0.000 -0.068 -0.034
ar.L24 0.0006 0.012 0.053 0.958 -0.022 0.023
ar.L25 -0.0517 0.007 -7.509 0.000 -0.065 -0.038
ar.L26 0.0098 0.011 0.910 0.363 -0.011 0.031
ar.L27 -0.0292 0.007 -4.238 0.000 -0.043 -0.016
ar.L28 0.0197 0.009 2.245 0.025 0.003 0.037
ar.L29 -0.0621 0.006 -9.753 0.000 -0.075 -0.050
ar.L30 0.0601 0.011 5.436 0.000 0.038 0.082
ma.L1 -0.3718 0.145 -2.565 0.010 -0.656 -0.088
sigma2 0.0114 4.64e-05 244.818 0.000 0.011 0.011
===================================================================================
Ljung-Box (L1) (Q): 0.17 Jarque-Bera (JB): 287650.55
Prob(Q): 0.68 Prob(JB): 0.00
Heteroskedasticity (H): 61.74 Skew: -0.71
Prob(H) (two-sided): 0.00 Kurtosis: 22.59
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17889 16.805598
17890 16.713067
17891 16.826916
17892 16.791374
17893 16.802531
17894 16.752224
17895 16.809814
17896 16.798400
17897 16.843114
17898 16.723802
Name: predicted_mean, dtype: float64
lower price upper price
17889 16.596626 17.014571
17890 16.500866 16.925267
17891 16.558479 17.095352
17892 16.516308 17.066441
17893 16.491568 17.113493
17894 16.433728 17.070721
17895 16.461090 17.158538
17896 16.442023 17.154778
17897 16.458590 17.227638
17898 16.332661 17.114943
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
Root Mean Squared Error (RMSE): 0.2451176952008188 Weighted Mean Absolute Percentage Error (WMAPE): 1.3418506846792075
def forecastplusyahoo(name,p,d,q):
global forecast_values_dict
train1 = pd.concat([data17, data18, data19, data20, data21, data22, data23])
test = data24
hourly_data = train1[train1['short_name'] == name]
print(hourly_data)
company_tickers = [f'{name}.IS'] # Add more ticker symbols as needed
# Fetching data for each company in the list
for ticker_symbol in company_tickers:
half_hourly_data = yf.download(ticker_symbol, start='2021-12-27', end='2023-09-24', interval='1h')
#print(f"Stock data for {ticker_symbol}:\n{stock_data}\n") # Display or save the retrieved stock data
# Extract 'Close' column from half-hourly data
hourly_price = hourly_data['price']
half_hourly_close = half_hourly_data['Close']
# Combine the close values from half-hourly data with hourly data
combined_data = pd.concat([hourly_price, half_hourly_close], axis=1)
combined_data.columns = ['price', 'Close'] # Rename columns for clarity
# Update 'price' with 'Close' values where 'Close' is not NaN
combined_data['price'] = combined_data['Close'].where(combined_data['Close'].notnull(), combined_data['price'])
combined_data.drop(columns='Close', inplace=True)
# Print the combined data
print(combined_data.head(50))
train2 = pd.concat([data1, data2, data3, data4, data5, data6, data7, data8, data9, data10, data11, data12, data13, data14, data15, data16])
hourly2 = train2[train2['short_name'] == name]
hourly2 = hourly2.drop(columns=['short_name'])
company_2_data = pd.concat([hourly2, combined_data])
print(company_2_data.tail(30))
company_2_datat = test[test['short_name'] == name]
company_2_datat = company_2_datat.drop(columns=['short_name'])
company_2_datatest = company_2_datat
mean_first_10 = company_2_datatest.head(10).mean()
print("Mean of the first 10 values:", mean_first_10)
print(company_2_datatest.head(10))
from statsmodels.tsa.stattools import adfuller
##stationary yapma
company_2_stationary = company_2_data.diff().dropna()
print(company_2_stationary)
# Assuming 'data' is your time series data
resultxx = adfuller(company_2_stationary)
print('ADF Statistic:', resultxx[0])
print('p-value:', resultxx[1])
print('Critical Values:', resultxx[4])
# stationary check, it is stationary(ADF lower than critical values and p-value close to 0)
model = ARIMA(company_2_data, order=(p, q, d))
results = model.fit()
print(results.summary())
forecast = results.get_prediction(start=len(company_2_data), end=len(company_2_data) + 9)
mean_forecast = forecast.predicted_mean
confidence_intervals = forecast.conf_int()
lower_limits = confidence_intervals.loc[:, 'lower price']
upper_limits = confidence_intervals.loc[:, 'upper price']
print(mean_forecast)
print(confidence_intervals)
# Assuming 'lower_limits' and 'upper_limits' contain the corresponding confidence intervals
# Assuming 'company_1_data2' contains your second dataset
plt.figure()
# Plotting the forecasted values and confidence intervals
plt.plot(mean_forecast.values, color='red', label='Forecast')
plt.fill_between(range(len(mean_forecast)), lower_limits, upper_limits, color='pink', alpha=0.3)
# Plotting the first 25 points of company_1_data2 as 'Actual Data'
plt.plot(range(10), company_2_datatest.iloc[:10], label='Actual Data', marker='o') # Adjust the slicing as needed
plt.xlabel('Index') # Update with appropriate label
plt.ylabel('Values') # Update with appropriate label
plt.title('Comparison between Forecast and Actual Data')
plt.legend()
plt.show()
from sklearn.metrics import mean_squared_error
import numpy as np
# Assuming 'actual_data' contains your actual data and 'mean_forecast' contains the mean forecast
# Convert 'actual_data' and 'mean_forecast' to numpy arrays if they are not already
actual_values = np.array(company_2_datatest.iloc[:10])
forecast_values = np.array(mean_forecast)
# Calculate RMSE
rmse = np.sqrt(mean_squared_error(actual_values, forecast_values))
print(f"Root Mean Squared Error (RMSE): {rmse}")
# Calculate WMAPE
weights = np.ones(len(actual_values)) # You can assign different weights if needed
wmape = (np.sum(weights * np.abs(actual_values - forecast_values)) / np.sum(weights * actual_values)) * 100
print(f"Weighted Mean Absolute Percentage Error (WMAPE): {wmape}")
forecast_values_dict[name] = forecast_values
forecast_values_dict
{'DOHOL': array([13.01306449, 13.04257918, 13.02973437, 13.07453275, 13.02937603,
13.05913917, 13.04881921, 13.082186 , 13.03374603, 13.00613119]),
'THYAO': array([226.13101751, 226.26852833, 226.42032994, 226.6087945 ,
226.70787386, 226.59144776, 226.55427092, 226.54366528,
226.41961549, 226.23183637]),
'AKBNK': array([30.80978808, 30.63629036, 30.88432712, 30.75440825, 30.75042825,
30.68261854, 30.87246131, 30.82266251, 30.87164805, 30.64957291]),
'ARCLK': array([155.06442848, 155.4578826 , 154.13914292, 156.07072694,
153.92670687, 156.1784913 , 153.63655976, 156.44945324,
153.92343616, 156.39371036]),
'ASELS': array([40.62093168, 40.72080647, 40.7926436 , 40.8386505 , 40.84498468,
40.92750797, 40.99724979, 41.0857884 , 41.16063593, 41.22911896]),
'BIMAS': array([273.54756357, 272.9005355 , 272.51219702, 272.84021062,
273.0163417 , 273.29856892, 272.83809248, 272.99803782,
273.1449719 , 272.82298172]),
'EKGYO': array([7.90707046, 7.90059903, 7.90056802, 7.92245672, 7.91757176,
7.93844774, 7.92907451, 7.94344171, 7.93026525, 7.92601819]),
'EREGL': array([43.42271402, 43.67570074, 43.68156492, 43.88839659, 44.03190447,
44.1858422 , 44.04997057, 44.14717663, 44.82232925, 44.70765931]),
'FROTO': array([830.99342011, 831.41658266, 816.41586429, 836.6674633 ,
815.88834072, 839.91271182, 814.74433573, 837.25802376,
815.20918087, 835.08460046]),
'GUBRF': array([339.87796362, 340.16697833, 340.50847152, 340.84338934,
340.99136619, 341.17266403, 341.26792064, 341.42562748,
341.55386215, 341.53078252]),
'GARAN': array([50.83791303, 50.47171095, 50.86708609, 50.64472572, 50.75083697,
50.60956213, 50.88829045, 50.82087524, 50.90530508, 50.53815156]),
'KRDMD': array([27.721452 , 27.77772753, 27.79666637, 28.00045428, 28.02067117,
28.04435518, 27.95967038, 28.07780282, 28.48802435, 28.51923712]),
'KCHOL': array([137.70920812, 137.78882709, 137.60628609, 137.50293096,
137.49009867, 137.91650468, 137.45401574, 137.74890979,
137.48307846, 137.51370898]),
'KOZAL': array([28.15296182, 27.81152165, 28.01582755, 27.85912797, 27.92805541,
27.79629562, 27.850601 , 27.810418 , 27.88405013, 27.75427053]),
'KOZAA': array([64.442217 , 64.49914624, 64.61765373, 64.63157838, 64.57373582,
64.53594664, 64.5626773 , 64.51316425, 64.49992953, 64.43331413]),
'PGSUS': array([767.56774435, 767.897003 , 768.60457119, 769.65587515,
770.17994418, 770.11791094, 770.48379815, 770.86778067,
770.69242384, 770.56229437]),
'PETKM': array([19.94173525, 19.97642075, 20.02653009, 20.0333102 , 20.07217744,
20.07708441, 20.13076748, 20.12609185, 20.19109692, 20.17880116]),
'SAHOL': array([56.56999413, 56.33595434, 56.33811775, 56.22998732, 56.2911494 ,
56.30971749, 56.22538251, 56.30170441, 56.16669052, 55.98690546]),
'SASA': array([45.47200588, 45.50652578, 45.44123154, 45.47844539, 45.41828789,
45.38917362, 45.34201528, 45.31660873, 45.26293691, 45.29011549]),
'SISE': array([54.65009571, 54.56645649, 54.49723824, 54.5159781 , 54.45040415,
54.51558208, 54.51294552, 54.67361163, 54.60204021, 54.59185697]),
'TAVHL': array([119.2118943 , 119.37612117, 119.42077278, 119.54144124,
119.46942857, 119.3852175 , 119.3559883 , 119.24559832,
119.24620871, 119.14375031]),
'TKFEN': array([53.08337418, 51.29917802, 52.16497887, 51.49742949, 51.80733827,
51.44371168, 51.71235968, 51.66294665, 51.96932133, 51.47331446]),
'TUPRS': array([157.13331746, 156.27664193, 152.93421588, 155.7690737 ,
152.24444009, 156.11603578, 151.89893673, 154.61978833,
150.4155922 , 153.22341825]),
'TTKOM': array([23.11897847, 23.14119281, 23.30755215, 23.29280935, 23.2157269 ,
23.22841065, 23.16826001, 23.23829154, 23.20723445, 23.17652782]),
'TCELL': array([54.35313425, 54.42334094, 54.415687 , 54.42949023, 54.41160504,
54.44819581, 54.34379083, 54.44854446, 54.34923009, 54.43303394]),
'HALKB': array([15.25031614, 15.19856817, 15.20964401, 15.20120812, 15.19495896,
15.19516409, 15.17884975, 15.16737502, 15.16180558, 15.14571866]),
'ISCTR': array([24.92934268, 24.77714168, 25.08263182, 25.00058133, 25.02540662,
24.82405421, 25.01017606, 25.19772373, 25.34944429, 25.09607709]),
'VAKBN': array([13.50670414, 13.49999039, 13.4996114 , 13.5083786 , 13.50868853,
13.50496283, 13.50066606, 13.50330453, 13.51232156, 13.50727618]),
'VESTL': array([64.12090282, 64.06530507, 64.03466852, 64.08277756, 64.10047854,
64.04165974, 64.00051075, 63.99428248, 64.05127422, 64.03409011]),
'YKBNK': array([16.80559848, 16.71306661, 16.82691555, 16.79137443, 16.80253076,
16.75222425, 16.80981406, 16.7984003 , 16.84311436, 16.72380225])}
Next, I have updated the up to date data in submission stage. I have made an another function since there is no test data now. Also, the yahoo finance data in the function code is updated every day since it can only give the last 2 years of data. I have also changed the parameters of those companies where the model overpredicted to 2,1,1 here.
#UP TO DATE DATA
import pandas as pd
data24= pd.read_csv('20230925_20231120_bist30.csv', index_col='timestamp', parse_dates=True)
data25=pd.read_csv('20231121_20231209_bist30.csv', index_col='timestamp', parse_dates=True)
data26= pd.read_csv('Daily_Series - 20231210_20240111.csv', index_col='timestamp', parse_dates=True)
print(data25.tail(50))
submissions= {}
price short_name timestamp 2023-12-04 09:00:00+03:00 19.83 YKBNK 2023-12-04 10:00:00+03:00 19.82 YKBNK 2023-12-04 11:00:00+03:00 19.78 YKBNK 2023-12-04 12:00:00+03:00 19.86 YKBNK 2023-12-04 13:00:00+03:00 19.78 YKBNK 2023-12-04 14:00:00+03:00 19.83 YKBNK 2023-12-04 15:00:00+03:00 19.90 YKBNK 2023-12-04 16:00:00+03:00 20.22 YKBNK 2023-12-04 17:00:00+03:00 20.50 YKBNK 2023-12-04 18:00:00+03:00 20.52 YKBNK 2023-12-05 09:00:00+03:00 20.42 YKBNK 2023-12-05 10:00:00+03:00 20.40 YKBNK 2023-12-05 11:00:00+03:00 20.34 YKBNK 2023-12-05 12:00:00+03:00 20.48 YKBNK 2023-12-05 13:00:00+03:00 20.32 YKBNK 2023-12-05 14:00:00+03:00 20.40 YKBNK 2023-12-05 15:00:00+03:00 20.32 YKBNK 2023-12-05 16:00:00+03:00 20.30 YKBNK 2023-12-05 17:00:00+03:00 20.24 YKBNK 2023-12-05 18:00:00+03:00 20.28 YKBNK 2023-12-06 09:00:00+03:00 20.28 YKBNK 2023-12-06 10:00:00+03:00 20.82 YKBNK 2023-12-06 11:00:00+03:00 20.64 YKBNK 2023-12-06 12:00:00+03:00 20.50 YKBNK 2023-12-06 13:00:00+03:00 20.60 YKBNK 2023-12-06 14:00:00+03:00 20.36 YKBNK 2023-12-06 15:00:00+03:00 20.56 YKBNK 2023-12-06 16:00:00+03:00 20.62 YKBNK 2023-12-06 17:00:00+03:00 20.50 YKBNK 2023-12-06 18:00:00+03:00 20.42 YKBNK 2023-12-07 09:00:00+03:00 20.26 YKBNK 2023-12-07 10:00:00+03:00 19.79 YKBNK 2023-12-07 11:00:00+03:00 19.54 YKBNK 2023-12-07 12:00:00+03:00 19.47 YKBNK 2023-12-07 13:00:00+03:00 19.56 YKBNK 2023-12-07 14:00:00+03:00 19.43 YKBNK 2023-12-07 15:00:00+03:00 19.40 YKBNK 2023-12-07 16:00:00+03:00 19.50 YKBNK 2023-12-07 17:00:00+03:00 19.76 YKBNK 2023-12-07 18:00:00+03:00 19.89 YKBNK 2023-12-08 09:00:00+03:00 19.95 YKBNK 2023-12-08 10:00:00+03:00 19.83 YKBNK 2023-12-08 11:00:00+03:00 19.88 YKBNK 2023-12-08 12:00:00+03:00 19.72 YKBNK 2023-12-08 13:00:00+03:00 19.69 YKBNK 2023-12-08 14:00:00+03:00 19.72 YKBNK 2023-12-08 15:00:00+03:00 19.74 YKBNK 2023-12-08 16:00:00+03:00 19.81 YKBNK 2023-12-08 17:00:00+03:00 19.67 YKBNK 2023-12-08 18:00:00+03:00 19.67 YKBNK
## updating the code everyday (data26 changes every day, and yahoofinance data can only have 2 years of data so updating its date is also necessary)
def forecastfinal(name,p,d,q):
global submissions
train1 = pd.concat([data17, data18, data19, data20, data21, data22, data23, data24,data25,data26])
hourly_data = train1[train1['short_name'] == name]
print(hourly_data)
company_tickers = [f'{name}.IS'] # Add more ticker symbols as needed
# Fetching data for each company in the list
for ticker_symbol in company_tickers:
half_hourly_data = yf.download(ticker_symbol, start='2022-01-12', end='2024-01-12', interval='1h')
# Display or save the retrieved stock data
# Extract 'Close' column from half-hourly data
hourly_price = hourly_data['price']
half_hourly_close = half_hourly_data['Close']
######
#UPDATING NEW DATA EVERYDAY!!!
###########
# new=yf.download(f'{name}.IS', start='2023-12-23', end='2023-12-26', interval='1h')
#new=new['Adj Close']
# Combine the close values from half-hourly data with hourly data
combined_data = pd.concat([hourly_price, half_hourly_close], axis=1)
# Update 'price' with 'Close' values where 'Close' is not NaN
combined_data['price'] = combined_data['Close'].where(combined_data['Close'].notnull(), combined_data['price'])
#combined_data['price'] = combined_data['Adj Close'].where(combined_data['Adj Close'].notnull(), combined_data['price'])
combined_data=combined_data['price']
# Print the combined data
print(combined_data.head(50))
train2 = pd.concat([data1, data2, data3, data4, data5, data6, data7, data8, data9, data10, data11, data12, data13, data14, data15, data16])
hourly2 = train2[train2['short_name'] == name]
hourly2 = hourly2['price']
company_2_data = pd.concat([hourly2, combined_data])
print(company_2_data.tail(30))
from statsmodels.tsa.stattools import adfuller
##stationary yapma
company_2_stationary = company_2_data.diff().dropna()
print(company_2_stationary)
# Assuming 'data' is your time series data
resultxx = adfuller(company_2_stationary)
print('ADF Statistic:', resultxx[0])
print('p-value:', resultxx[1])
print('Critical Values:', resultxx[4])
# stationary check, it is stationary(ADF lower than critical values and p-value close to 0)
model = ARIMA(company_2_data, order=(p, q, d))
results = model.fit()
print(results.summary())
forecast = results.get_prediction(start=len(company_2_data), end=len(company_2_data) + 9)
mean_forecast = forecast.predicted_mean
confidence_intervals = forecast.conf_int()
lower_limits = confidence_intervals.loc[:, 'lower price']
upper_limits = confidence_intervals.loc[:, 'upper price']
forecast_values = np.array(mean_forecast)
print(mean_forecast)
print(confidence_intervals)
# Assuming 'lower_limits' and 'upper_limits' contain the corresponding confidence intervals
# Assuming 'company_1_data2' contains your second dataset
submissions[name] = forecast_values
forecastfinal('THYAO', 30, 1, 3)
forecastfinal('AKBNK', 30, 1, 1)
forecastfinal('ARCLK', 30, 1, 1)
forecastfinal('ASELS', 30, 1, 3)
forecastfinal('BIMAS', 30, 1, 1)
forecastfinal('DOHOL', 2, 1, 1)
forecastfinal('EKGYO', 2, 1, 1)
forecastfinal('EREGL', 30, 1, 3)
forecastfinal('FROTO', 30, 1, 1)
forecastfinal('GUBRF', 30, 1, 3)
forecastfinal('GARAN', 30, 1, 1)
forecastfinal('KRDMD', 30, 1, 3)
forecastfinal('KCHOL', 30, 1, 3)
forecastfinal('KOZAL', 30, 1, 1)
forecastfinal('KOZAA', 30, 1, 3)
forecastfinal('PGSUS', 30, 1, 3)
forecastfinal('PETKM', 30, 1, 3)
forecastfinal('SAHOL', 30, 1, 1)
forecastfinal('SASA', 30, 1, 1)
forecastfinal('SISE', 30, 1, 1)
forecastfinal('TAVHL', 30, 1, 3)
forecastfinal('TKFEN', 30, 1, 1)
forecastfinal('TUPRS', 30, 1, 3)
forecastfinal('TTKOM', 30, 1, 3)
forecastfinal('TCELL', 30, 1, 1)
forecastfinal('HALKB', 30, 1, 1)
forecastfinal('ISCTR', 30, 1, 1)
forecastfinal('VAKBN', 30, 1, 1)
forecastfinal('VESTL', 30, 1, 1)
forecastfinal('YKBNK', 30, 1, 1)
price short_name
timestamp
2021-12-27 09:00:00+03:00 21.04 THYAO
2021-12-27 10:00:00+03:00 21.54 THYAO
2021-12-27 11:00:00+03:00 21.20 THYAO
2021-12-27 12:00:00+03:00 21.18 THYAO
2021-12-27 13:00:00+03:00 21.14 THYAO
... ... ...
2024-01-11 14:00:00+03:00 247.30 THYAO
2024-01-11 15:00:00+03:00 245.40 THYAO
2024-01-11 16:00:00+03:00 246.20 THYAO
2024-01-11 17:00:00+03:00 246.70 THYAO
2024-01-11 18:00:00+03:00 246.70 THYAO
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 21.04
2021-12-27 07:00:00+00:00 21.54
2021-12-27 08:00:00+00:00 21.20
2021-12-27 09:00:00+00:00 21.18
2021-12-27 10:00:00+00:00 21.14
2021-12-27 11:00:00+00:00 20.88
2021-12-27 12:00:00+00:00 20.86
2021-12-27 13:00:00+00:00 21.04
2021-12-27 14:00:00+00:00 20.78
2021-12-27 15:00:00+00:00 20.78
2021-12-28 06:00:00+00:00 20.86
2021-12-28 07:00:00+00:00 21.06
2021-12-28 08:00:00+00:00 20.94
2021-12-28 09:00:00+00:00 20.98
2021-12-28 10:00:00+00:00 20.92
2021-12-28 11:00:00+00:00 20.78
2021-12-28 12:00:00+00:00 20.78
2021-12-28 13:00:00+00:00 20.48
2021-12-28 14:00:00+00:00 20.60
2021-12-28 15:00:00+00:00 20.48
2021-12-29 06:00:00+00:00 20.08
2021-12-29 07:00:00+00:00 20.04
2021-12-29 08:00:00+00:00 20.70
2021-12-29 09:00:00+00:00 20.88
2021-12-29 10:00:00+00:00 20.86
2021-12-29 11:00:00+00:00 20.94
2021-12-29 12:00:00+00:00 21.10
2021-12-29 13:00:00+00:00 21.52
2021-12-29 14:00:00+00:00 21.20
2021-12-29 15:00:00+00:00 21.08
2021-12-30 06:00:00+00:00 21.42
2021-12-30 07:00:00+00:00 21.18
2021-12-30 08:00:00+00:00 21.10
2021-12-30 09:00:00+00:00 20.72
2021-12-30 10:00:00+00:00 20.56
2021-12-30 11:00:00+00:00 20.22
2021-12-30 12:00:00+00:00 20.36
2021-12-30 13:00:00+00:00 20.50
2021-12-30 14:00:00+00:00 20.30
2021-12-30 15:00:00+00:00 20.14
2021-12-31 06:00:00+00:00 20.30
2021-12-31 07:00:00+00:00 20.68
2021-12-31 08:00:00+00:00 20.70
2021-12-31 09:00:00+00:00 20.94
2021-12-31 10:00:00+00:00 20.50
2021-12-31 11:00:00+00:00 20.46
2021-12-31 12:00:00+00:00 20.32
2021-12-31 13:00:00+00:00 20.28
2021-12-31 14:00:00+00:00 19.98
2021-12-31 15:00:00+00:00 20.02
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 246.000000
2024-01-10 10:00:00+00:00 248.200000
2024-01-10 10:30:00+00:00 247.699997
2024-01-10 11:00:00+00:00 247.900000
2024-01-10 11:30:00+00:00 249.100006
2024-01-10 12:00:00+00:00 248.400000
2024-01-10 12:30:00+00:00 249.500000
2024-01-10 13:00:00+00:00 249.800000
2024-01-10 13:30:00+00:00 250.500000
2024-01-10 14:00:00+00:00 250.500000
2024-01-10 15:00:00+00:00 249.900000
2024-01-11 06:00:00+00:00 250.500000
2024-01-11 06:30:00+00:00 249.100006
2024-01-11 07:00:00+00:00 246.800000
2024-01-11 07:30:00+00:00 246.800003
2024-01-11 08:00:00+00:00 246.400000
2024-01-11 08:30:00+00:00 247.699997
2024-01-11 09:00:00+00:00 247.800000
2024-01-11 09:30:00+00:00 248.000000
2024-01-11 10:00:00+00:00 247.400000
2024-01-11 10:30:00+00:00 247.000000
2024-01-11 11:00:00+00:00 247.300000
2024-01-11 11:30:00+00:00 246.300003
2024-01-11 12:00:00+00:00 245.400000
2024-01-11 12:30:00+00:00 247.699997
2024-01-11 13:00:00+00:00 246.200000
2024-01-11 13:30:00+00:00 245.699997
2024-01-11 14:00:00+00:00 246.700000
2024-01-11 14:30:00+00:00 246.699997
2024-01-11 15:00:00+00:00 246.700000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.220000
2018-01-02 11:00:00+03:00 0.040000
2018-01-02 12:00:00+03:00 0.000000
2018-01-02 13:00:00+03:00 0.010000
2018-01-02 14:00:00+03:00 -0.010000
...
2024-01-11 13:00:00+00:00 -1.499997
2024-01-11 13:30:00+00:00 -0.500003
2024-01-11 14:00:00+00:00 1.000003
2024-01-11 14:30:00+00:00 -0.000003
2024-01-11 15:00:00+00:00 0.000003
Name: price, Length: 19222, dtype: float64
ADF Statistic: -27.880607006689658
p-value: 0.0
Critical Values: {'1%': -3.430690705077546, '5%': -2.8616905793225387, '10%': -2.566850149319263}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 3, 1) Log Likelihood -18009.821
Date: Thu, 11 Jan 2024 AIC 36083.642
Time: 21:10:52 BIC 36335.280
Sample: 0 HQIC 36166.129
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.8990 0.003 -320.736 0.000 -0.904 -0.893
ar.L2 -0.8762 0.004 -213.244 0.000 -0.884 -0.868
ar.L3 -0.8496 0.006 -145.051 0.000 -0.861 -0.838
ar.L4 -0.8169 0.007 -116.546 0.000 -0.831 -0.803
ar.L5 -0.7846 0.008 -101.223 0.000 -0.800 -0.769
ar.L6 -0.7351 0.008 -87.672 0.000 -0.752 -0.719
ar.L7 -0.6982 0.009 -77.136 0.000 -0.716 -0.680
ar.L8 -0.6801 0.010 -70.938 0.000 -0.699 -0.661
ar.L9 -0.6448 0.010 -65.053 0.000 -0.664 -0.625
ar.L10 -0.5969 0.010 -60.564 0.000 -0.616 -0.578
ar.L11 -0.5554 0.010 -55.306 0.000 -0.575 -0.536
ar.L12 -0.5262 0.010 -50.818 0.000 -0.547 -0.506
ar.L13 -0.4867 0.011 -46.225 0.000 -0.507 -0.466
ar.L14 -0.4488 0.011 -41.602 0.000 -0.470 -0.428
ar.L15 -0.4353 0.011 -40.123 0.000 -0.457 -0.414
ar.L16 -0.3978 0.011 -37.321 0.000 -0.419 -0.377
ar.L17 -0.3511 0.011 -32.970 0.000 -0.372 -0.330
ar.L18 -0.3289 0.011 -31.304 0.000 -0.349 -0.308
ar.L19 -0.3197 0.010 -31.029 0.000 -0.340 -0.300
ar.L20 -0.3330 0.010 -32.893 0.000 -0.353 -0.313
ar.L21 -0.3292 0.010 -32.179 0.000 -0.349 -0.309
ar.L22 -0.3219 0.010 -33.654 0.000 -0.341 -0.303
ar.L23 -0.2970 0.010 -30.809 0.000 -0.316 -0.278
ar.L24 -0.2847 0.010 -29.730 0.000 -0.303 -0.266
ar.L25 -0.2170 0.009 -24.340 0.000 -0.234 -0.200
ar.L26 -0.1617 0.008 -19.120 0.000 -0.178 -0.145
ar.L27 -0.1356 0.008 -17.658 0.000 -0.151 -0.121
ar.L28 -0.1006 0.007 -14.905 0.000 -0.114 -0.087
ar.L29 -0.0745 0.006 -13.437 0.000 -0.085 -0.064
ar.L30 -0.0371 0.004 -9.340 0.000 -0.045 -0.029
ma.L1 -1.0000 0.006 -180.533 0.000 -1.011 -0.989
sigma2 0.3811 0.002 195.105 0.000 0.377 0.385
===================================================================================
Ljung-Box (L1) (Q): 0.03 Jarque-Bera (JB): 1860122.24
Prob(Q): 0.87 Prob(JB): 0.00
Heteroskedasticity (H): 78.71 Skew: 2.22
Prob(H) (two-sided): 0.00 Kurtosis: 50.99
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 246.728447
19224 246.787834
19225 246.901565
19226 247.047472
19227 247.219273
19228 247.206833
19229 247.091589
19230 247.069706
19231 246.883991
19232 246.690849
Name: predicted_mean, dtype: float64
lower price upper price
19223 245.518519 247.938375
19224 244.988161 248.587507
19225 244.638452 249.164677
19226 244.380381 249.714562
19227 244.179175 250.259371
19228 243.812869 250.600797
19229 243.347308 250.835871
19230 242.980262 251.159151
19231 242.461020 251.306961
19232 241.936427 251.445271
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 6.6937 AKBNK
2021-12-27 10:00:00+03:00 6.7991 AKBNK
2021-12-27 11:00:00+03:00 6.8343 AKBNK
2021-12-27 12:00:00+03:00 6.8430 AKBNK
2021-12-27 13:00:00+03:00 6.8255 AKBNK
... ... ...
2024-01-11 14:00:00+03:00 41.1200 AKBNK
2024-01-11 15:00:00+03:00 41.1600 AKBNK
2024-01-11 16:00:00+03:00 41.3200 AKBNK
2024-01-11 17:00:00+03:00 41.1200 AKBNK
2024-01-11 18:00:00+03:00 41.1200 AKBNK
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 6.6937
2021-12-27 07:00:00+00:00 6.7991
2021-12-27 08:00:00+00:00 6.8343
2021-12-27 09:00:00+00:00 6.8430
2021-12-27 10:00:00+00:00 6.8255
2021-12-27 11:00:00+00:00 6.7816
2021-12-27 12:00:00+00:00 6.7904
2021-12-27 13:00:00+00:00 6.8343
2021-12-27 14:00:00+00:00 6.8782
2021-12-27 15:00:00+00:00 6.8782
2021-12-28 06:00:00+00:00 6.8430
2021-12-28 07:00:00+00:00 6.6850
2021-12-28 08:00:00+00:00 6.6323
2021-12-28 09:00:00+00:00 6.6147
2021-12-28 10:00:00+00:00 6.5884
2021-12-28 11:00:00+00:00 6.5884
2021-12-28 12:00:00+00:00 6.6410
2021-12-28 13:00:00+00:00 6.5708
2021-12-28 14:00:00+00:00 6.4917
2021-12-28 15:00:00+00:00 6.4653
2021-12-29 06:00:00+00:00 6.3775
2021-12-29 07:00:00+00:00 6.3599
2021-12-29 08:00:00+00:00 6.4477
2021-12-29 09:00:00+00:00 6.4389
2021-12-29 10:00:00+00:00 6.4389
2021-12-29 11:00:00+00:00 6.4214
2021-12-29 12:00:00+00:00 6.4741
2021-12-29 13:00:00+00:00 6.5005
2021-12-29 14:00:00+00:00 6.4653
2021-12-29 15:00:00+00:00 6.4565
2021-12-30 06:00:00+00:00 6.4741
2021-12-30 07:00:00+00:00 6.5357
2021-12-30 08:00:00+00:00 6.5796
2021-12-30 09:00:00+00:00 6.5796
2021-12-30 10:00:00+00:00 6.5796
2021-12-30 11:00:00+00:00 6.5269
2021-12-30 12:00:00+00:00 6.5269
2021-12-30 13:00:00+00:00 6.5444
2021-12-30 14:00:00+00:00 6.4653
2021-12-30 15:00:00+00:00 6.4477
2021-12-31 06:00:00+00:00 6.4565
2021-12-31 07:00:00+00:00 6.4565
2021-12-31 08:00:00+00:00 6.5093
2021-12-31 09:00:00+00:00 6.5005
2021-12-31 10:00:00+00:00 6.4829
2021-12-31 11:00:00+00:00 6.4741
2021-12-31 12:00:00+00:00 6.4214
2021-12-31 13:00:00+00:00 6.3775
2021-12-31 14:00:00+00:00 6.2984
2021-12-31 15:00:00+00:00 6.3248
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 39.740002
2024-01-10 10:00:00+00:00 39.860000
2024-01-10 10:30:00+00:00 40.000000
2024-01-10 11:00:00+00:00 39.940000
2024-01-10 11:30:00+00:00 40.020000
2024-01-10 12:00:00+00:00 39.960000
2024-01-10 12:30:00+00:00 40.139999
2024-01-10 13:00:00+00:00 40.820000
2024-01-10 13:30:00+00:00 40.720001
2024-01-10 14:00:00+00:00 40.740000
2024-01-10 15:00:00+00:00 40.740000
2024-01-11 06:00:00+00:00 40.980000
2024-01-11 06:30:00+00:00 41.160000
2024-01-11 07:00:00+00:00 40.720000
2024-01-11 07:30:00+00:00 40.980000
2024-01-11 08:00:00+00:00 40.920000
2024-01-11 08:30:00+00:00 41.119999
2024-01-11 09:00:00+00:00 41.160000
2024-01-11 09:30:00+00:00 41.200001
2024-01-11 10:00:00+00:00 41.200000
2024-01-11 10:30:00+00:00 41.160000
2024-01-11 11:00:00+00:00 41.120000
2024-01-11 11:30:00+00:00 41.160000
2024-01-11 12:00:00+00:00 41.160000
2024-01-11 12:30:00+00:00 41.419998
2024-01-11 13:00:00+00:00 41.320000
2024-01-11 13:30:00+00:00 40.980000
2024-01-11 14:00:00+00:00 41.120000
2024-01-11 14:30:00+00:00 41.119999
2024-01-11 15:00:00+00:00 41.120000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.112700
2018-01-02 11:00:00+03:00 0.035200
2018-01-02 12:00:00+03:00 -0.014000
2018-01-02 13:00:00+03:00 0.021000
2018-01-02 14:00:00+03:00 0.035300
...
2024-01-11 13:00:00+00:00 -0.099998
2024-01-11 13:30:00+00:00 -0.340000
2024-01-11 14:00:00+00:00 0.140000
2024-01-11 14:30:00+00:00 -0.000001
2024-01-11 15:00:00+00:00 0.000001
Name: price, Length: 19223, dtype: float64
ADF Statistic: -21.016923841077208
p-value: 0.0
Critical Values: {'1%': -3.4306909715527216, '5%': -2.8616906970881657, '10%': -2.5668502120039105}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19224
Model: ARIMA(30, 1, 1) Log Likelihood 2573.790
Date: Thu, 11 Jan 2024 AIC -5083.580
Time: 21:13:47 BIC -4831.936
Sample: 0 HQIC -5001.092
- 19224
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.6352 0.133 -4.779 0.000 -0.896 -0.375
ar.L2 0.0872 0.104 0.842 0.400 -0.116 0.290
ar.L3 -0.0333 0.006 -5.633 0.000 -0.045 -0.022
ar.L4 0.0203 0.008 2.654 0.008 0.005 0.035
ar.L5 -0.0371 0.006 -5.902 0.000 -0.049 -0.025
ar.L6 0.0170 0.008 2.184 0.029 0.002 0.032
ar.L7 -0.0249 0.007 -3.613 0.000 -0.038 -0.011
ar.L8 0.0181 0.008 2.314 0.021 0.003 0.033
ar.L9 -0.0222 0.008 -2.917 0.004 -0.037 -0.007
ar.L10 0.0492 0.008 6.381 0.000 0.034 0.064
ar.L11 0.0215 0.009 2.444 0.015 0.004 0.039
ar.L12 -0.0164 0.008 -2.111 0.035 -0.032 -0.001
ar.L13 -0.0098 0.006 -1.642 0.101 -0.022 0.002
ar.L14 -0.0308 0.006 -5.118 0.000 -0.043 -0.019
ar.L15 -0.0173 0.007 -2.478 0.013 -0.031 -0.004
ar.L16 -0.1657 0.005 -30.735 0.000 -0.176 -0.155
ar.L17 -0.0741 0.023 -3.240 0.001 -0.119 -0.029
ar.L18 0.3654 0.014 27.041 0.000 0.339 0.392
ar.L19 0.3634 0.047 7.790 0.000 0.272 0.455
ar.L20 -0.1933 0.055 -3.515 0.000 -0.301 -0.086
ar.L21 -0.1393 0.018 -7.599 0.000 -0.175 -0.103
ar.L22 -0.0266 0.022 -1.225 0.221 -0.069 0.016
ar.L23 -0.0559 0.008 -6.640 0.000 -0.072 -0.039
ar.L24 0.0081 0.010 0.783 0.434 -0.012 0.028
ar.L25 -0.0301 0.007 -4.586 0.000 -0.043 -0.017
ar.L26 0.0308 0.008 3.924 0.000 0.015 0.046
ar.L27 -0.0146 0.008 -1.860 0.063 -0.030 0.001
ar.L28 0.0082 0.007 1.146 0.252 -0.006 0.022
ar.L29 -0.0494 0.007 -7.296 0.000 -0.063 -0.036
ar.L30 0.0548 0.011 5.103 0.000 0.034 0.076
ma.L1 -0.1417 0.133 -1.067 0.286 -0.402 0.119
sigma2 0.0446 0.000 240.002 0.000 0.044 0.045
===================================================================================
Ljung-Box (L1) (Q): 0.07 Jarque-Bera (JB): 242419.32
Prob(Q): 0.79 Prob(JB): 0.00
Heteroskedasticity (H): 30.90 Skew: -0.42
Prob(H) (two-sided): 0.00 Kurtosis: 20.38
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224 41.214986
19225 41.003356
19226 40.975632
19227 41.094561
19228 41.061683
19229 41.157111
19230 41.064339
19231 41.144549
19232 41.062722
19233 41.052960
Name: predicted_mean, dtype: float64
lower price upper price
19224 40.801192 41.628779
19225 40.579389 41.427323
19226 40.436767 41.514497
19227 40.538270 41.650851
19228 40.428833 41.694532
19229 40.506502 41.807721
19230 40.351493 41.777184
19231 40.415053 41.874045
19232 40.278098 41.847345
19233 40.253389 41.852530
price short_name
timestamp
2021-12-27 09:00:00+03:00 49.8145 ARCLK
2021-12-27 10:00:00+03:00 49.6728 ARCLK
2021-12-27 11:00:00+03:00 49.4837 ARCLK
2021-12-27 12:00:00+03:00 48.9639 ARCLK
2021-12-27 13:00:00+03:00 49.3419 ARCLK
... ... ...
2024-01-11 14:00:00+03:00 130.2000 ARCLK
2024-01-11 15:00:00+03:00 129.5000 ARCLK
2024-01-11 16:00:00+03:00 129.6000 ARCLK
2024-01-11 17:00:00+03:00 129.8000 ARCLK
2024-01-11 18:00:00+03:00 129.5000 ARCLK
[5104 rows x 2 columns]
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 49.8145
2021-12-27 07:00:00+00:00 49.6728
2021-12-27 08:00:00+00:00 49.4837
2021-12-27 09:00:00+00:00 48.9639
2021-12-27 10:00:00+00:00 49.3419
2021-12-27 11:00:00+00:00 48.7747
2021-12-27 12:00:00+00:00 48.1131
2021-12-27 13:00:00+00:00 48.3022
2021-12-27 14:00:00+00:00 48.3494
2021-12-27 15:00:00+00:00 48.1131
2021-12-28 06:00:00+00:00 48.4439
2021-12-28 07:00:00+00:00 48.4439
2021-12-28 08:00:00+00:00 48.6803
2021-12-28 09:00:00+00:00 48.4439
2021-12-28 10:00:00+00:00 48.1131
2021-12-28 11:00:00+00:00 47.7350
2021-12-28 12:00:00+00:00 47.4987
2021-12-28 13:00:00+00:00 47.3569
2021-12-28 14:00:00+00:00 46.7898
2021-12-28 15:00:00+00:00 46.8276
2021-12-29 06:00:00+00:00 46.5062
2021-12-29 07:00:00+00:00 46.5062
2021-12-29 08:00:00+00:00 47.4987
2021-12-29 09:00:00+00:00 47.4041
2021-12-29 10:00:00+00:00 47.3569
2021-12-29 11:00:00+00:00 47.4041
2021-12-29 12:00:00+00:00 47.4041
2021-12-29 13:00:00+00:00 47.4041
2021-12-29 14:00:00+00:00 47.3097
2021-12-29 15:00:00+00:00 47.4514
2021-12-30 06:00:00+00:00 47.6404
2021-12-30 07:00:00+00:00 47.3097
2021-12-30 08:00:00+00:00 47.4514
2021-12-30 09:00:00+00:00 47.2624
2021-12-30 10:00:00+00:00 47.0922
2021-12-30 11:00:00+00:00 46.4873
2021-12-30 12:00:00+00:00 46.6196
2021-12-30 13:00:00+00:00 46.6574
2021-12-30 14:00:00+00:00 46.3550
2021-12-30 15:00:00+00:00 46.1281
2021-12-31 06:00:00+00:00 46.2226
2021-12-31 07:00:00+00:00 46.0335
2021-12-31 08:00:00+00:00 46.3927
2021-12-31 09:00:00+00:00 46.4494
2021-12-31 10:00:00+00:00 46.0335
2021-12-31 11:00:00+00:00 46.2793
2021-12-31 12:00:00+00:00 46.1848
2021-12-31 13:00:00+00:00 46.2226
2021-12-31 14:00:00+00:00 45.9391
2021-12-31 15:00:00+00:00 45.7499
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 128.399994
2024-01-10 10:00:00+00:00 128.300000
2024-01-10 10:30:00+00:00 128.399994
2024-01-10 11:00:00+00:00 128.600000
2024-01-10 11:30:00+00:00 128.600006
2024-01-10 12:00:00+00:00 128.000000
2024-01-10 12:30:00+00:00 128.800003
2024-01-10 13:00:00+00:00 129.900000
2024-01-10 13:30:00+00:00 129.600006
2024-01-10 14:00:00+00:00 130.600000
2024-01-10 15:00:00+00:00 130.100000
2024-01-11 06:00:00+00:00 130.900000
2024-01-11 06:30:00+00:00 131.100006
2024-01-11 07:00:00+00:00 130.500000
2024-01-11 07:30:00+00:00 130.000000
2024-01-11 08:00:00+00:00 129.600000
2024-01-11 08:30:00+00:00 130.399994
2024-01-11 09:00:00+00:00 130.300000
2024-01-11 09:30:00+00:00 130.199997
2024-01-11 10:00:00+00:00 130.000000
2024-01-11 10:30:00+00:00 129.899994
2024-01-11 11:00:00+00:00 130.200000
2024-01-11 11:30:00+00:00 130.000000
2024-01-11 12:00:00+00:00 129.500000
2024-01-11 12:30:00+00:00 130.699997
2024-01-11 13:00:00+00:00 129.600000
2024-01-11 13:30:00+00:00 129.699997
2024-01-11 14:00:00+00:00 129.800000
2024-01-11 14:30:00+00:00 129.500000
2024-01-11 15:00:00+00:00 129.500000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.085300
2018-01-02 11:00:00+03:00 -0.119500
2018-01-02 12:00:00+03:00 -0.017100
2018-01-02 13:00:00+03:00 0.000000
2018-01-02 14:00:00+03:00 0.102500
...
2024-01-11 13:00:00+00:00 -1.099997
2024-01-11 13:30:00+00:00 0.099997
2024-01-11 14:00:00+00:00 0.100003
2024-01-11 14:30:00+00:00 -0.300000
2024-01-11 15:00:00+00:00 0.000000
Name: price, Length: 19223, dtype: float64
ADF Statistic: -19.99276333206753
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19224
Model: ARIMA(30, 1, 1) Log Likelihood -19909.038
Date: Thu, 11 Jan 2024 AIC 39882.076
Time: 21:17:02 BIC 40133.720
Sample: 0 HQIC 39964.565
- 19224
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.2642 0.005 -249.021 0.000 -1.274 -1.254
ar.L2 -0.2695 0.006 -48.446 0.000 -0.280 -0.259
ar.L3 0.0120 0.006 1.881 0.060 -0.001 0.025
ar.L4 -0.0218 0.006 -3.401 0.001 -0.034 -0.009
ar.L5 -0.0008 0.006 -0.120 0.905 -0.013 0.012
ar.L6 0.0011 0.006 0.164 0.870 -0.011 0.014
ar.L7 0.0006 0.006 0.096 0.924 -0.012 0.013
ar.L8 0.0085 0.007 1.258 0.208 -0.005 0.022
ar.L9 0.0075 0.007 1.100 0.271 -0.006 0.021
ar.L10 0.0101 0.007 1.489 0.136 -0.003 0.023
ar.L11 -0.0136 0.007 -1.972 0.049 -0.027 -8.31e-05
ar.L12 -0.0184 0.007 -2.696 0.007 -0.032 -0.005
ar.L13 0.0036 0.007 0.512 0.609 -0.010 0.018
ar.L14 0.0151 0.007 2.121 0.034 0.001 0.029
ar.L15 0.0386 0.007 5.831 0.000 0.026 0.052
ar.L16 -0.0370 0.007 -5.377 0.000 -0.051 -0.024
ar.L17 -0.0441 0.007 -6.759 0.000 -0.057 -0.031
ar.L18 0.0652 0.006 10.735 0.000 0.053 0.077
ar.L19 0.2315 0.005 43.741 0.000 0.221 0.242
ar.L20 0.0334 0.005 6.128 0.000 0.023 0.044
ar.L21 -0.1456 0.006 -22.893 0.000 -0.158 -0.133
ar.L22 -0.0372 0.007 -5.220 0.000 -0.051 -0.023
ar.L23 -0.0353 0.007 -5.092 0.000 -0.049 -0.022
ar.L24 -0.0170 0.007 -2.491 0.013 -0.030 -0.004
ar.L25 -0.0142 0.007 -2.070 0.038 -0.028 -0.001
ar.L26 0.0013 0.007 0.191 0.849 -0.012 0.015
ar.L27 -0.0192 0.007 -2.678 0.007 -0.033 -0.005
ar.L28 -0.0233 0.007 -3.310 0.001 -0.037 -0.009
ar.L29 0.0295 0.007 4.221 0.000 0.016 0.043
ar.L30 0.0730 0.005 14.572 0.000 0.063 0.083
ma.L1 0.8940 0.004 232.990 0.000 0.886 0.901
sigma2 0.4639 0.002 260.055 0.000 0.460 0.467
===================================================================================
Ljung-Box (L1) (Q): 0.88 Jarque-Bera (JB): 197613.42
Prob(Q): 0.35 Prob(JB): 0.00
Heteroskedasticity (H): 83.65 Skew: 0.90
Prob(H) (two-sided): 0.00 Kurtosis: 18.60
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224 129.639104
19225 129.565381
19226 129.328436
19227 129.401643
19228 129.315117
19229 129.606430
19230 129.437279
19231 129.512839
19232 129.415826
19233 129.598379
Name: predicted_mean, dtype: float64
lower price upper price
19224 128.304145 130.974062
19225 127.987774 131.142987
19226 127.401898 131.254974
19227 127.266747 131.536540
19228 126.936687 131.693548
19229 127.045673 132.167187
19230 126.675710 132.198847
19231 126.589479 132.436199
19232 126.313576 132.518077
19233 126.348872 132.847885
price short_name
timestamp
2021-12-27 09:00:00+03:00 10.9361 ASELS
2021-12-27 10:00:00+03:00 11.2048 ASELS
2021-12-27 11:00:00+03:00 11.1252 ASELS
2021-12-27 12:00:00+03:00 11.0655 ASELS
2021-12-27 13:00:00+03:00 11.0754 ASELS
... ... ...
2024-01-11 14:00:00+03:00 47.1000 ASELS
2024-01-11 15:00:00+03:00 46.8000 ASELS
2024-01-11 16:00:00+03:00 46.8000 ASELS
2024-01-11 17:00:00+03:00 46.7200 ASELS
2024-01-11 18:00:00+03:00 46.8800 ASELS
[5104 rows x 2 columns]
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 10.9361
2021-12-27 07:00:00+00:00 11.2048
2021-12-27 08:00:00+00:00 11.1252
2021-12-27 09:00:00+00:00 11.0655
2021-12-27 10:00:00+00:00 11.0754
2021-12-27 11:00:00+00:00 10.9660
2021-12-27 12:00:00+00:00 10.9063
2021-12-27 13:00:00+00:00 10.8565
2021-12-27 14:00:00+00:00 10.8466
2021-12-27 15:00:00+00:00 10.8565
2021-12-28 06:00:00+00:00 10.9560
2021-12-28 07:00:00+00:00 10.9759
2021-12-28 08:00:00+00:00 10.8266
2021-12-28 09:00:00+00:00 10.7570
2021-12-28 10:00:00+00:00 10.7172
2021-12-28 11:00:00+00:00 10.6376
2021-12-28 12:00:00+00:00 10.7172
2021-12-28 13:00:00+00:00 10.6177
2021-12-28 14:00:00+00:00 10.4983
2021-12-28 15:00:00+00:00 10.5281
2021-12-29 06:00:00+00:00 10.4485
2021-12-29 07:00:00+00:00 10.3590
2021-12-29 08:00:00+00:00 10.6077
2021-12-29 09:00:00+00:00 10.7072
2021-12-29 10:00:00+00:00 10.7072
2021-12-29 11:00:00+00:00 10.7371
2021-12-29 12:00:00+00:00 10.7570
2021-12-29 13:00:00+00:00 10.8366
2021-12-29 14:00:00+00:00 10.7371
2021-12-29 15:00:00+00:00 10.7669
2021-12-30 06:00:00+00:00 10.9162
2021-12-30 07:00:00+00:00 10.7371
2021-12-30 08:00:00+00:00 10.8067
2021-12-30 09:00:00+00:00 10.7470
2021-12-30 10:00:00+00:00 10.7371
2021-12-30 11:00:00+00:00 10.5679
2021-12-30 12:00:00+00:00 10.6077
2021-12-30 13:00:00+00:00 10.6276
2021-12-30 14:00:00+00:00 10.5281
2021-12-30 15:00:00+00:00 10.5082
2021-12-31 06:00:00+00:00 10.5878
2021-12-31 07:00:00+00:00 10.5182
2021-12-31 08:00:00+00:00 10.5381
2021-12-31 09:00:00+00:00 10.5878
2021-12-31 10:00:00+00:00 10.4684
2021-12-31 11:00:00+00:00 10.5082
2021-12-31 12:00:00+00:00 10.4485
2021-12-31 13:00:00+00:00 10.4286
2021-12-31 14:00:00+00:00 10.3689
2021-12-31 15:00:00+00:00 10.3888
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 47.259998
2024-01-10 10:00:00+00:00 47.340000
2024-01-10 10:30:00+00:00 47.400002
2024-01-10 11:00:00+00:00 47.560000
2024-01-10 11:30:00+00:00 47.580002
2024-01-10 12:00:00+00:00 47.380000
2024-01-10 12:30:00+00:00 47.740002
2024-01-10 13:00:00+00:00 47.860000
2024-01-10 13:30:00+00:00 47.560001
2024-01-10 14:00:00+00:00 47.620000
2024-01-10 15:00:00+00:00 47.480000
2024-01-11 06:00:00+00:00 47.760000
2024-01-11 06:30:00+00:00 47.580002
2024-01-11 07:00:00+00:00 47.300000
2024-01-11 07:30:00+00:00 47.340000
2024-01-11 08:00:00+00:00 47.220000
2024-01-11 08:30:00+00:00 47.279999
2024-01-11 09:00:00+00:00 47.280000
2024-01-11 09:30:00+00:00 47.160000
2024-01-11 10:00:00+00:00 47.200000
2024-01-11 10:30:00+00:00 47.160000
2024-01-11 11:00:00+00:00 47.100000
2024-01-11 11:30:00+00:00 47.040001
2024-01-11 12:00:00+00:00 46.800000
2024-01-11 12:30:00+00:00 47.160000
2024-01-11 13:00:00+00:00 46.800000
2024-01-11 13:30:00+00:00 46.820000
2024-01-11 14:00:00+00:00 46.720000
2024-01-11 14:30:00+00:00 46.880001
2024-01-11 15:00:00+00:00 46.880000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.014400
2018-01-02 11:00:00+03:00 -0.014400
2018-01-02 12:00:00+03:00 0.028900
2018-01-02 13:00:00+03:00 0.004800
2018-01-02 14:00:00+03:00 -0.009600
...
2024-01-11 13:00:00+00:00 -0.360000
2024-01-11 13:30:00+00:00 0.020000
2024-01-11 14:00:00+00:00 -0.100000
2024-01-11 14:30:00+00:00 0.160001
2024-01-11 15:00:00+00:00 -0.000001
Name: price, Length: 19113, dtype: float64
ADF Statistic: -20.989558933444666
p-value: 0.0
Critical Values: {'1%': -3.4306929924764797, '5%': -2.8616915902117444, '10%': -2.5668506873985146}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19114
Model: ARIMA(30, 3, 1) Log Likelihood 9714.185
Date: Thu, 11 Jan 2024 AIC -19364.370
Time: 21:20:41 BIC -19112.914
Sample: 0 HQIC -19281.920
- 19114
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.9564 0.003 -276.375 0.000 -0.963 -0.950
ar.L2 -0.8940 0.005 -182.493 0.000 -0.904 -0.884
ar.L3 -0.9071 0.006 -140.067 0.000 -0.920 -0.894
ar.L4 -0.8447 0.008 -104.861 0.000 -0.860 -0.829
ar.L5 -0.8353 0.009 -91.433 0.000 -0.853 -0.817
ar.L6 -0.8062 0.010 -78.091 0.000 -0.826 -0.786
ar.L7 -0.8021 0.011 -72.948 0.000 -0.824 -0.781
ar.L8 -0.7566 0.011 -66.156 0.000 -0.779 -0.734
ar.L9 -0.7532 0.012 -63.689 0.000 -0.776 -0.730
ar.L10 -0.6867 0.012 -56.338 0.000 -0.711 -0.663
ar.L11 -0.6313 0.013 -49.595 0.000 -0.656 -0.606
ar.L12 -0.5957 0.012 -48.075 0.000 -0.620 -0.571
ar.L13 -0.5622 0.013 -43.692 0.000 -0.587 -0.537
ar.L14 -0.5370 0.013 -40.609 0.000 -0.563 -0.511
ar.L15 -0.5052 0.013 -38.212 0.000 -0.531 -0.479
ar.L16 -0.4749 0.013 -35.714 0.000 -0.501 -0.449
ar.L17 -0.4199 0.013 -32.168 0.000 -0.445 -0.394
ar.L18 -0.4147 0.013 -31.783 0.000 -0.440 -0.389
ar.L19 -0.3895 0.013 -30.490 0.000 -0.415 -0.364
ar.L20 -0.3879 0.013 -30.975 0.000 -0.412 -0.363
ar.L21 -0.3948 0.012 -31.959 0.000 -0.419 -0.371
ar.L22 -0.3527 0.012 -28.474 0.000 -0.377 -0.328
ar.L23 -0.2953 0.012 -24.200 0.000 -0.319 -0.271
ar.L24 -0.2314 0.012 -19.148 0.000 -0.255 -0.208
ar.L25 -0.1947 0.011 -17.461 0.000 -0.217 -0.173
ar.L26 -0.1317 0.010 -12.931 0.000 -0.152 -0.112
ar.L27 -0.1180 0.009 -12.735 0.000 -0.136 -0.100
ar.L28 -0.0689 0.008 -8.452 0.000 -0.085 -0.053
ar.L29 -0.0545 0.006 -8.427 0.000 -0.067 -0.042
ar.L30 -0.0154 0.004 -3.498 0.000 -0.024 -0.007
ma.L1 -0.9960 0.002 -625.674 0.000 -0.999 -0.993
sigma2 0.0208 6.05e-05 344.160 0.000 0.021 0.021
===================================================================================
Ljung-Box (L1) (Q): 8.07 Jarque-Bera (JB): 975374.60
Prob(Q): 0.00 Prob(JB): 0.00
Heteroskedasticity (H): 22.98 Skew: 1.25
Prob(H) (two-sided): 0.00 Kurtosis: 37.91
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19114 46.886176
19115 46.845436
19116 46.851228
19117 46.816103
19118 46.822895
19119 46.784912
19120 46.747803
19121 46.714902
19122 46.665398
19123 46.638604
Name: predicted_mean, dtype: float64
lower price upper price
19114 46.603404 47.168947
19115 46.435909 47.254963
19116 46.334199 47.368257
19117 46.210830 47.421376
19118 46.131348 47.514441
19119 46.014450 47.555374
19120 45.900809 47.594798
19121 45.796426 47.633378
19122 45.674477 47.656320
19123 45.578761 47.698448
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 62.9265 BIMAS
2021-12-27 10:00:00+03:00 63.4541 BIMAS
2021-12-27 11:00:00+03:00 62.8305 BIMAS
2021-12-27 12:00:00+03:00 62.5908 BIMAS
2021-12-27 13:00:00+03:00 62.4948 BIMAS
... ... ...
2024-01-11 14:00:00+03:00 325.0000 BIMAS
2024-01-11 15:00:00+03:00 323.5000 BIMAS
2024-01-11 16:00:00+03:00 324.5000 BIMAS
2024-01-11 17:00:00+03:00 327.2500 BIMAS
2024-01-11 18:00:00+03:00 328.2500 BIMAS
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 62.9265
2021-12-27 07:00:00+00:00 63.4541
2021-12-27 08:00:00+00:00 62.8305
2021-12-27 09:00:00+00:00 62.5908
2021-12-27 10:00:00+00:00 62.4948
2021-12-27 11:00:00+00:00 61.7753
2021-12-27 12:00:00+00:00 61.7753
2021-12-27 13:00:00+00:00 61.7274
2021-12-27 14:00:00+00:00 61.4396
2021-12-27 15:00:00+00:00 61.2957
2021-12-28 06:00:00+00:00 61.8714
2021-12-28 07:00:00+00:00 61.8233
2021-12-28 08:00:00+00:00 61.3916
2021-12-28 09:00:00+00:00 61.0080
2021-12-28 10:00:00+00:00 61.1519
2021-12-28 11:00:00+00:00 60.7202
2021-12-28 12:00:00+00:00 61.2478
2021-12-28 13:00:00+00:00 60.6243
2021-12-28 14:00:00+00:00 60.0007
2021-12-28 15:00:00+00:00 60.0488
2021-12-29 06:00:00+00:00 60.0967
2021-12-29 07:00:00+00:00 60.5764
2021-12-29 08:00:00+00:00 61.3916
2021-12-29 09:00:00+00:00 61.4396
2021-12-29 10:00:00+00:00 61.4396
2021-12-29 11:00:00+00:00 61.3437
2021-12-29 12:00:00+00:00 61.3437
2021-12-29 13:00:00+00:00 61.2957
2021-12-29 14:00:00+00:00 61.0559
2021-12-29 15:00:00+00:00 61.1040
2021-12-30 06:00:00+00:00 61.5835
2021-12-30 07:00:00+00:00 60.6722
2021-12-30 08:00:00+00:00 60.5764
2021-12-30 09:00:00+00:00 60.4805
2021-12-30 10:00:00+00:00 60.2405
2021-12-30 11:00:00+00:00 59.4732
2021-12-30 12:00:00+00:00 59.5691
2021-12-30 13:00:00+00:00 59.7130
2021-12-30 14:00:00+00:00 59.2813
2021-12-30 15:00:00+00:00 59.1375
2021-12-31 06:00:00+00:00 59.4253
2021-12-31 07:00:00+00:00 59.4253
2021-12-31 08:00:00+00:00 59.4732
2021-12-31 09:00:00+00:00 59.6170
2021-12-31 10:00:00+00:00 59.5212
2021-12-31 11:00:00+00:00 59.4732
2021-12-31 12:00:00+00:00 59.4253
2021-12-31 13:00:00+00:00 59.4732
2021-12-31 14:00:00+00:00 59.0895
2021-12-31 15:00:00+00:00 58.8017
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 315.50
2024-01-10 10:00:00+00:00 314.00
2024-01-10 10:30:00+00:00 314.25
2024-01-10 11:00:00+00:00 314.75
2024-01-10 11:30:00+00:00 314.50
2024-01-10 12:00:00+00:00 314.50
2024-01-10 12:30:00+00:00 319.00
2024-01-10 13:00:00+00:00 320.50
2024-01-10 13:30:00+00:00 318.25
2024-01-10 14:00:00+00:00 318.75
2024-01-10 15:00:00+00:00 318.25
2024-01-11 06:00:00+00:00 320.75
2024-01-11 06:30:00+00:00 324.25
2024-01-11 07:00:00+00:00 323.75
2024-01-11 07:30:00+00:00 324.75
2024-01-11 08:00:00+00:00 324.00
2024-01-11 08:30:00+00:00 329.00
2024-01-11 09:00:00+00:00 327.00
2024-01-11 09:30:00+00:00 325.75
2024-01-11 10:00:00+00:00 325.00
2024-01-11 10:30:00+00:00 325.75
2024-01-11 11:00:00+00:00 325.00
2024-01-11 11:30:00+00:00 325.25
2024-01-11 12:00:00+00:00 323.50
2024-01-11 12:30:00+00:00 327.00
2024-01-11 13:00:00+00:00 324.50
2024-01-11 13:30:00+00:00 325.25
2024-01-11 14:00:00+00:00 327.25
2024-01-11 14:30:00+00:00 328.25
2024-01-11 15:00:00+00:00 328.25
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.3467
2018-01-02 11:00:00+03:00 -0.1226
2018-01-02 12:00:00+03:00 -0.1833
2018-01-02 13:00:00+03:00 0.1426
2018-01-02 14:00:00+03:00 0.1428
...
2024-01-11 13:00:00+00:00 -2.5000
2024-01-11 13:30:00+00:00 0.7500
2024-01-11 14:00:00+00:00 2.0000
2024-01-11 14:30:00+00:00 1.0000
2024-01-11 15:00:00+00:00 0.0000
Name: price, Length: 19222, dtype: float64
ADF Statistic: -20.907466583883348
p-value: 0.0
Critical Values: {'1%': -3.4306910426831823, '5%': -2.86169072852345, '10%': -2.5668502287363797}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 1, 1) Log Likelihood -26542.801
Date: Thu, 11 Jan 2024 AIC 53149.602
Time: 21:23:33 BIC 53401.244
Sample: 0 HQIC 53232.090
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.2134 0.006 -194.614 0.000 -1.226 -1.201
ar.L2 -0.2345 0.006 -42.536 0.000 -0.245 -0.224
ar.L3 -0.0232 0.007 -3.542 0.000 -0.036 -0.010
ar.L4 -0.0281 0.008 -3.686 0.000 -0.043 -0.013
ar.L5 0.0047 0.007 0.645 0.519 -0.010 0.019
ar.L6 0.0176 0.007 2.484 0.013 0.004 0.032
ar.L7 0.0352 0.007 5.012 0.000 0.021 0.049
ar.L8 0.0261 0.007 3.497 0.000 0.011 0.041
ar.L9 0.0329 0.008 4.304 0.000 0.018 0.048
ar.L10 0.0450 0.008 5.975 0.000 0.030 0.060
ar.L11 0.0473 0.008 6.135 0.000 0.032 0.062
ar.L12 0.0253 0.007 3.420 0.001 0.011 0.040
ar.L13 0.0263 0.007 3.821 0.000 0.013 0.040
ar.L14 0.0189 0.007 2.619 0.009 0.005 0.033
ar.L15 0.0278 0.007 3.771 0.000 0.013 0.042
ar.L16 -0.0193 0.008 -2.550 0.011 -0.034 -0.004
ar.L17 -0.0417 0.007 -5.774 0.000 -0.056 -0.028
ar.L18 0.0792 0.006 12.662 0.000 0.067 0.091
ar.L19 0.1835 0.005 39.260 0.000 0.174 0.193
ar.L20 -0.0059 0.005 -1.108 0.268 -0.016 0.005
ar.L21 -0.1056 0.006 -16.790 0.000 -0.118 -0.093
ar.L22 -0.0403 0.007 -5.718 0.000 -0.054 -0.027
ar.L23 -0.0596 0.008 -7.758 0.000 -0.075 -0.045
ar.L24 -0.0326 0.008 -4.124 0.000 -0.048 -0.017
ar.L25 -0.0146 0.008 -1.829 0.067 -0.030 0.001
ar.L26 0.0015 0.008 0.181 0.856 -0.015 0.018
ar.L27 -0.0176 0.008 -2.246 0.025 -0.033 -0.002
ar.L28 -0.0392 0.008 -5.018 0.000 -0.054 -0.024
ar.L29 -0.0059 0.008 -0.730 0.465 -0.022 0.010
ar.L30 0.0600 0.006 10.263 0.000 0.049 0.071
ma.L1 0.8783 0.005 172.835 0.000 0.868 0.888
sigma2 0.9266 0.003 299.084 0.000 0.921 0.933
===================================================================================
Ljung-Box (L1) (Q): 0.12 Jarque-Bera (JB): 584358.02
Prob(Q): 0.73 Prob(JB): 0.00
Heteroskedasticity (H): 30.42 Skew: 0.28
Prob(H) (two-sided): 0.00 Kurtosis: 30.01
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 328.780623
19224 328.660012
19225 328.225624
19226 328.257666
19227 328.299201
19228 328.808080
19229 328.013440
19230 328.242287
19231 328.077706
19232 328.298680
Name: predicted_mean, dtype: float64
lower price upper price
19223 326.893950 330.667296
19224 326.394340 330.925683
19225 325.463965 330.987282
19226 325.209720 331.305612
19227 324.890420 331.707983
19228 325.150414 332.465746
19229 324.044233 331.982646
19230 324.037818 332.446756
19231 323.595485 332.559927
19232 323.590514 333.006846
price short_name
timestamp
2021-12-27 09:00:00+03:00 2.6366 DOHOL
2021-12-27 10:00:00+03:00 2.6736 DOHOL
2021-12-27 11:00:00+03:00 2.6366 DOHOL
2021-12-27 12:00:00+03:00 2.6458 DOHOL
2021-12-27 13:00:00+03:00 2.6458 DOHOL
... ... ...
2024-01-11 14:00:00+03:00 12.0200 DOHOL
2024-01-11 15:00:00+03:00 11.9200 DOHOL
2024-01-11 16:00:00+03:00 12.0000 DOHOL
2024-01-11 17:00:00+03:00 11.9900 DOHOL
2024-01-11 18:00:00+03:00 11.9900 DOHOL
[5104 rows x 2 columns]
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 2.6366
2021-12-27 07:00:00+00:00 2.6736
2021-12-27 08:00:00+00:00 2.6366
2021-12-27 09:00:00+00:00 2.6458
2021-12-27 10:00:00+00:00 2.6458
2021-12-27 11:00:00+00:00 2.6180
2021-12-27 12:00:00+00:00 2.6086
2021-12-27 13:00:00+00:00 2.6086
2021-12-27 14:00:00+00:00 2.5901
2021-12-27 15:00:00+00:00 2.5809
2021-12-28 06:00:00+00:00 2.6086
2021-12-28 07:00:00+00:00 2.6366
2021-12-28 08:00:00+00:00 2.6086
2021-12-28 09:00:00+00:00 2.5901
2021-12-28 10:00:00+00:00 2.6086
2021-12-28 11:00:00+00:00 2.5901
2021-12-28 12:00:00+00:00 2.5901
2021-12-28 13:00:00+00:00 2.5715
2021-12-28 14:00:00+00:00 2.5529
2021-12-28 15:00:00+00:00 2.5437
2021-12-29 06:00:00+00:00 2.5622
2021-12-29 07:00:00+00:00 2.5529
2021-12-29 08:00:00+00:00 2.5809
2021-12-29 09:00:00+00:00 2.5901
2021-12-29 10:00:00+00:00 2.5901
2021-12-29 11:00:00+00:00 2.5901
2021-12-29 12:00:00+00:00 2.5901
2021-12-29 13:00:00+00:00 2.6272
2021-12-29 14:00:00+00:00 2.6180
2021-12-29 15:00:00+00:00 2.6180
2021-12-30 06:00:00+00:00 2.6550
2021-12-30 07:00:00+00:00 2.6366
2021-12-30 08:00:00+00:00 2.6180
2021-12-30 09:00:00+00:00 2.6086
2021-12-30 10:00:00+00:00 2.6180
2021-12-30 11:00:00+00:00 2.5809
2021-12-30 12:00:00+00:00 2.5901
2021-12-30 13:00:00+00:00 2.5993
2021-12-30 14:00:00+00:00 2.5993
2021-12-30 15:00:00+00:00 2.5809
2021-12-31 06:00:00+00:00 2.5809
2021-12-31 07:00:00+00:00 2.5809
2021-12-31 08:00:00+00:00 2.5993
2021-12-31 09:00:00+00:00 2.5901
2021-12-31 10:00:00+00:00 2.5901
2021-12-31 11:00:00+00:00 2.5901
2021-12-31 12:00:00+00:00 2.5809
2021-12-31 13:00:00+00:00 2.5809
2021-12-31 14:00:00+00:00 2.5437
2021-12-31 15:00:00+00:00 2.5529
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 11.96
2024-01-10 10:00:00+00:00 11.97
2024-01-10 10:30:00+00:00 11.99
2024-01-10 11:00:00+00:00 12.03
2024-01-10 11:30:00+00:00 12.08
2024-01-10 12:00:00+00:00 11.98
2024-01-10 12:30:00+00:00 12.07
2024-01-10 13:00:00+00:00 12.06
2024-01-10 13:30:00+00:00 12.08
2024-01-10 14:00:00+00:00 12.11
2024-01-10 15:00:00+00:00 12.11
2024-01-11 06:00:00+00:00 12.15
2024-01-11 06:30:00+00:00 12.15
2024-01-11 07:00:00+00:00 12.06
2024-01-11 07:30:00+00:00 12.04
2024-01-11 08:00:00+00:00 12.01
2024-01-11 08:30:00+00:00 12.07
2024-01-11 09:00:00+00:00 12.08
2024-01-11 09:30:00+00:00 12.07
2024-01-11 10:00:00+00:00 12.03
2024-01-11 10:30:00+00:00 12.00
2024-01-11 11:00:00+00:00 12.02
2024-01-11 11:30:00+00:00 12.02
2024-01-11 12:00:00+00:00 11.92
2024-01-11 12:30:00+00:00 12.04
2024-01-11 13:00:00+00:00 12.00
2024-01-11 13:30:00+00:00 11.99
2024-01-11 14:00:00+00:00 11.99
2024-01-11 14:30:00+00:00 11.99
2024-01-11 15:00:00+00:00 11.99
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 8.200000e-03
2018-01-02 11:00:00+03:00 0.000000e+00
2018-01-02 12:00:00+03:00 0.000000e+00
2018-01-02 13:00:00+03:00 7.900000e-03
2018-01-02 14:00:00+03:00 -7.900000e-03
...
2024-01-11 13:00:00+00:00 -3.999996e-02
2024-01-11 13:30:00+00:00 -1.000023e-02
2024-01-11 14:00:00+00:00 2.288818e-07
2024-01-11 14:30:00+00:00 -2.288818e-07
2024-01-11 15:00:00+00:00 2.288818e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -22.845154701723008
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(2, 1, 1) Log Likelihood 23303.212
Date: Thu, 11 Jan 2024 AIC -46598.424
Time: 21:23:42 BIC -46566.969
Sample: 0 HQIC -46588.113
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.1872 0.011 -103.925 0.000 -1.210 -1.165
ar.L2 -0.2229 0.010 -21.260 0.000 -0.243 -0.202
ma.L1 0.6019 0.010 60.047 0.000 0.582 0.622
sigma2 0.0052 2.11e-05 245.445 0.000 0.005 0.005
===================================================================================
Ljung-Box (L1) (Q): 1.62 Jarque-Bera (JB): 190640.66
Prob(Q): 0.20 Prob(JB): 0.00
Heteroskedasticity (H): 80.60 Skew: 0.73
Prob(H) (two-sided): 0.00 Kurtosis: 18.36
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 11.993648
19224 11.989317
19225 11.993646
19226 11.989472
19227 11.993463
19228 11.989655
19229 11.993286
19230 11.989823
19231 11.993125
19232 11.989977
Name: predicted_mean, dtype: float64
lower price upper price
19223 11.852567 12.134729
19224 11.836585 12.142049
19225 11.796220 12.191072
19226 11.781794 12.197149
19227 11.752796 12.234130
19228 11.739767 12.239542
19229 11.716488 12.270085
19230 11.704391 12.275255
19231 11.684737 12.301514
19232 11.673326 12.306627
price short_name
timestamp
2021-12-27 09:00:00+03:00 2.0714 EKGYO
2021-12-27 10:00:00+03:00 2.0993 EKGYO
2021-12-27 11:00:00+03:00 2.0993 EKGYO
2021-12-27 12:00:00+03:00 2.0901 EKGYO
2021-12-27 13:00:00+03:00 2.0901 EKGYO
... ... ...
2024-01-11 14:00:00+03:00 8.2100 EKGYO
2024-01-11 15:00:00+03:00 8.1500 EKGYO
2024-01-11 16:00:00+03:00 8.1300 EKGYO
2024-01-11 17:00:00+03:00 8.1700 EKGYO
2024-01-11 18:00:00+03:00 8.1700 EKGYO
[5104 rows x 2 columns]
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 2.0714
2021-12-27 07:00:00+00:00 2.0993
2021-12-27 08:00:00+00:00 2.0993
2021-12-27 09:00:00+00:00 2.0901
2021-12-27 10:00:00+00:00 2.0901
2021-12-27 11:00:00+00:00 2.0620
2021-12-27 12:00:00+00:00 2.0807
2021-12-27 13:00:00+00:00 2.0901
2021-12-27 14:00:00+00:00 2.0714
2021-12-27 15:00:00+00:00 2.0714
2021-12-28 06:00:00+00:00 2.1086
2021-12-28 07:00:00+00:00 2.1460
2021-12-28 08:00:00+00:00 2.1273
2021-12-28 09:00:00+00:00 2.0901
2021-12-28 10:00:00+00:00 2.0901
2021-12-28 11:00:00+00:00 2.0620
2021-12-28 12:00:00+00:00 2.0807
2021-12-28 13:00:00+00:00 2.0714
2021-12-28 14:00:00+00:00 2.0527
2021-12-28 15:00:00+00:00 2.0527
2021-12-29 06:00:00+00:00 2.0620
2021-12-29 07:00:00+00:00 2.0247
2021-12-29 08:00:00+00:00 2.0247
2021-12-29 09:00:00+00:00 2.0247
2021-12-29 10:00:00+00:00 2.0341
2021-12-29 11:00:00+00:00 2.0341
2021-12-29 12:00:00+00:00 2.0620
2021-12-29 13:00:00+00:00 2.0620
2021-12-29 14:00:00+00:00 2.0433
2021-12-29 15:00:00+00:00 2.0527
2021-12-30 06:00:00+00:00 2.0714
2021-12-30 07:00:00+00:00 2.0527
2021-12-30 08:00:00+00:00 2.0527
2021-12-30 09:00:00+00:00 2.0433
2021-12-30 10:00:00+00:00 2.0620
2021-12-30 11:00:00+00:00 2.0341
2021-12-30 12:00:00+00:00 2.0620
2021-12-30 13:00:00+00:00 2.0620
2021-12-30 14:00:00+00:00 2.0433
2021-12-30 15:00:00+00:00 2.0433
2021-12-31 06:00:00+00:00 2.0527
2021-12-31 07:00:00+00:00 2.0433
2021-12-31 08:00:00+00:00 2.0527
2021-12-31 09:00:00+00:00 2.0433
2021-12-31 10:00:00+00:00 2.0433
2021-12-31 11:00:00+00:00 2.0341
2021-12-31 12:00:00+00:00 2.0433
2021-12-31 13:00:00+00:00 2.0247
2021-12-31 14:00:00+00:00 1.9967
2021-12-31 15:00:00+00:00 1.9967
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 8.07
2024-01-10 10:00:00+00:00 8.12
2024-01-10 10:30:00+00:00 8.14
2024-01-10 11:00:00+00:00 8.13
2024-01-10 11:30:00+00:00 8.13
2024-01-10 12:00:00+00:00 8.11
2024-01-10 12:30:00+00:00 8.15
2024-01-10 13:00:00+00:00 8.15
2024-01-10 13:30:00+00:00 8.23
2024-01-10 14:00:00+00:00 8.22
2024-01-10 15:00:00+00:00 8.25
2024-01-11 06:00:00+00:00 8.25
2024-01-11 06:30:00+00:00 8.27
2024-01-11 07:00:00+00:00 8.22
2024-01-11 07:30:00+00:00 8.20
2024-01-11 08:00:00+00:00 8.20
2024-01-11 08:30:00+00:00 8.20
2024-01-11 09:00:00+00:00 8.21
2024-01-11 09:30:00+00:00 8.22
2024-01-11 10:00:00+00:00 8.21
2024-01-11 10:30:00+00:00 8.22
2024-01-11 11:00:00+00:00 8.21
2024-01-11 11:30:00+00:00 8.22
2024-01-11 12:00:00+00:00 8.15
2024-01-11 12:30:00+00:00 8.22
2024-01-11 13:00:00+00:00 8.13
2024-01-11 13:30:00+00:00 8.14
2024-01-11 14:00:00+00:00 8.17
2024-01-11 14:30:00+00:00 8.17
2024-01-11 15:00:00+00:00 8.17
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 2.460000e-02
2018-01-02 11:00:00+03:00 -8.100000e-03
2018-01-02 12:00:00+03:00 8.100000e-03
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 8.100000e-03
...
2024-01-11 13:00:00+00:00 -9.000027e-02
2024-01-11 13:30:00+00:00 1.000034e-02
2024-01-11 14:00:00+00:00 2.999966e-02
2024-01-11 14:30:00+00:00 7.629395e-08
2024-01-11 15:00:00+00:00 -7.629395e-08
Name: price, Length: 19223, dtype: float64
ADF Statistic: -20.102925907656232
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19224
Model: ARIMA(2, 1, 1) Log Likelihood 29396.153
Date: Thu, 11 Jan 2024 AIC -58784.307
Time: 21:23:59 BIC -58752.851
Sample: 0 HQIC -58773.996
- 19224
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.2271 0.007 -188.312 0.000 -1.240 -1.214
ar.L2 -0.2469 0.006 -40.505 0.000 -0.259 -0.235
ma.L1 0.7118 0.005 132.477 0.000 0.701 0.722
sigma2 0.0027 9.47e-06 290.256 0.000 0.003 0.003
===================================================================================
Ljung-Box (L1) (Q): 0.95 Jarque-Bera (JB): 299599.18
Prob(Q): 0.33 Prob(JB): 0.00
Heteroskedasticity (H): 39.00 Skew: 0.91
Prob(H) (two-sided): 0.00 Kurtosis: 22.26
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224 8.176089
19225 8.168617
19226 8.176283
19227 8.168721
19228 8.176107
19229 8.168911
19230 8.175917
19231 8.169097
19232 8.175736
19233 8.169273
Name: predicted_mean, dtype: float64
lower price upper price
19224 8.073310 8.278869
19225 8.054400 8.282834
19226 8.031222 8.321343
19227 8.013970 8.323473
19228 7.998226 8.353988
19229 7.982723 8.355100
19230 7.970492 8.381343
19231 7.956214 8.381980
19232 7.946150 8.405322
19233 7.932800 8.405746
price short_name
timestamp
2021-12-27 09:00:00+03:00 25.0042 EREGL
2021-12-27 10:00:00+03:00 25.6008 EREGL
2021-12-27 11:00:00+03:00 25.4955 EREGL
2021-12-27 12:00:00+03:00 25.3902 EREGL
2021-12-27 13:00:00+03:00 25.5130 EREGL
... ... ...
2024-01-11 14:00:00+03:00 44.2400 EREGL
2024-01-11 15:00:00+03:00 44.0600 EREGL
2024-01-11 16:00:00+03:00 43.8600 EREGL
2024-01-11 17:00:00+03:00 43.8600 EREGL
2024-01-11 18:00:00+03:00 43.9000 EREGL
[5104 rows x 2 columns]
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 25.0042
2021-12-27 07:00:00+00:00 25.6008
2021-12-27 08:00:00+00:00 25.4955
2021-12-27 09:00:00+00:00 25.3902
2021-12-27 10:00:00+00:00 25.5130
2021-12-27 11:00:00+00:00 25.3376
2021-12-27 12:00:00+00:00 25.3726
2021-12-27 13:00:00+00:00 25.4077
2021-12-27 14:00:00+00:00 25.1972
2021-12-27 15:00:00+00:00 25.1972
2021-12-28 06:00:00+00:00 25.5130
2021-12-28 07:00:00+00:00 25.4253
2021-12-28 08:00:00+00:00 25.2849
2021-12-28 09:00:00+00:00 25.2674
2021-12-28 10:00:00+00:00 25.3200
2021-12-28 11:00:00+00:00 25.0217
2021-12-28 12:00:00+00:00 25.0568
2021-12-28 13:00:00+00:00 25.0393
2021-12-28 14:00:00+00:00 24.4602
2021-12-28 15:00:00+00:00 24.4076
2021-12-29 06:00:00+00:00 24.4076
2021-12-29 07:00:00+00:00 24.9515
2021-12-29 08:00:00+00:00 25.2498
2021-12-29 09:00:00+00:00 25.2323
2021-12-29 10:00:00+00:00 25.4779
2021-12-29 11:00:00+00:00 25.2674
2021-12-29 12:00:00+00:00 25.4779
2021-12-29 13:00:00+00:00 25.4428
2021-12-29 14:00:00+00:00 25.6885
2021-12-29 15:00:00+00:00 25.6885
2021-12-30 06:00:00+00:00 26.2324
2021-12-30 07:00:00+00:00 25.6183
2021-12-30 08:00:00+00:00 25.6885
2021-12-30 09:00:00+00:00 25.5657
2021-12-30 10:00:00+00:00 25.5130
2021-12-30 11:00:00+00:00 24.9515
2021-12-30 12:00:00+00:00 25.0744
2021-12-30 13:00:00+00:00 25.0744
2021-12-30 14:00:00+00:00 24.9866
2021-12-30 15:00:00+00:00 24.8813
2021-12-31 06:00:00+00:00 24.8989
2021-12-31 07:00:00+00:00 25.0217
2021-12-31 08:00:00+00:00 24.9866
2021-12-31 09:00:00+00:00 25.0042
2021-12-31 10:00:00+00:00 24.6883
2021-12-31 11:00:00+00:00 24.7410
2021-12-31 12:00:00+00:00 24.7234
2021-12-31 13:00:00+00:00 25.0042
2021-12-31 14:00:00+00:00 24.6708
2021-12-31 15:00:00+00:00 24.7059
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 45.119999
2024-01-10 10:00:00+00:00 44.760000
2024-01-10 10:30:00+00:00 44.980000
2024-01-10 11:00:00+00:00 44.860000
2024-01-10 11:30:00+00:00 44.799999
2024-01-10 12:00:00+00:00 44.780000
2024-01-10 12:30:00+00:00 44.900002
2024-01-10 13:00:00+00:00 44.980000
2024-01-10 13:30:00+00:00 44.900002
2024-01-10 14:00:00+00:00 45.100000
2024-01-10 15:00:00+00:00 45.040000
2024-01-11 06:00:00+00:00 44.880000
2024-01-11 06:30:00+00:00 44.880001
2024-01-11 07:00:00+00:00 44.480000
2024-01-11 07:30:00+00:00 44.060001
2024-01-11 08:00:00+00:00 44.300000
2024-01-11 08:30:00+00:00 44.259998
2024-01-11 09:00:00+00:00 44.340000
2024-01-11 09:30:00+00:00 44.240002
2024-01-11 10:00:00+00:00 44.220000
2024-01-11 10:30:00+00:00 44.240002
2024-01-11 11:00:00+00:00 44.240000
2024-01-11 11:30:00+00:00 44.220001
2024-01-11 12:00:00+00:00 44.060000
2024-01-11 12:30:00+00:00 44.380001
2024-01-11 13:00:00+00:00 43.860000
2024-01-11 13:30:00+00:00 43.980000
2024-01-11 14:00:00+00:00 43.860000
2024-01-11 14:30:00+00:00 43.900002
2024-01-11 15:00:00+00:00 43.900000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.017800
2018-01-02 11:00:00+03:00 -0.023700
2018-01-02 12:00:00+03:00 0.000000
2018-01-02 13:00:00+03:00 0.011800
2018-01-02 14:00:00+03:00 0.023700
...
2024-01-11 13:00:00+00:00 -0.520001
2024-01-11 13:30:00+00:00 0.120000
2024-01-11 14:00:00+00:00 -0.120000
2024-01-11 14:30:00+00:00 0.040002
2024-01-11 15:00:00+00:00 -0.000002
Name: price, Length: 19222, dtype: float64
ADF Statistic: -22.17100995605609
p-value: 0.0
Critical Values: {'1%': -3.4306910604704344, '5%': -2.86169073638432, '10%': -2.566850232920588}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 3, 1) Log Likelihood -916.606
Date: Thu, 11 Jan 2024 AIC 1897.212
Time: 21:26:54 BIC 2148.850
Sample: 0 HQIC 1979.699
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.3563 0.003 -437.973 0.000 -1.362 -1.350
ar.L2 -1.2180 0.007 -180.717 0.000 -1.231 -1.205
ar.L3 -1.3049 0.010 -132.570 0.000 -1.324 -1.286
ar.L4 -1.2078 0.013 -93.329 0.000 -1.233 -1.182
ar.L5 -1.2639 0.015 -83.103 0.000 -1.294 -1.234
ar.L6 -1.1693 0.017 -67.339 0.000 -1.203 -1.135
ar.L7 -1.1623 0.019 -59.887 0.000 -1.200 -1.124
ar.L8 -1.0995 0.021 -52.261 0.000 -1.141 -1.058
ar.L9 -1.0244 0.022 -46.282 0.000 -1.068 -0.981
ar.L10 -0.9585 0.023 -41.756 0.000 -1.003 -0.913
ar.L11 -0.9117 0.024 -38.046 0.000 -0.959 -0.865
ar.L12 -0.8869 0.025 -36.030 0.000 -0.935 -0.839
ar.L13 -0.7952 0.025 -31.542 0.000 -0.845 -0.746
ar.L14 -0.7561 0.025 -29.786 0.000 -0.806 -0.706
ar.L15 -0.7148 0.025 -28.304 0.000 -0.764 -0.665
ar.L16 -0.7651 0.025 -30.482 0.000 -0.814 -0.716
ar.L17 -0.8891 0.025 -35.681 0.000 -0.938 -0.840
ar.L18 -0.3093 0.025 -12.377 0.000 -0.358 -0.260
ar.L19 -0.2443 0.024 -10.223 0.000 -0.291 -0.197
ar.L20 -0.4299 0.023 -18.895 0.000 -0.475 -0.385
ar.L21 -0.3978 0.022 -18.139 0.000 -0.441 -0.355
ar.L22 -0.4476 0.021 -21.520 0.000 -0.488 -0.407
ar.L23 -0.3591 0.020 -18.021 0.000 -0.398 -0.320
ar.L24 -0.3559 0.019 -19.210 0.000 -0.392 -0.320
ar.L25 -0.2763 0.017 -16.219 0.000 -0.310 -0.243
ar.L26 -0.2215 0.016 -14.240 0.000 -0.252 -0.191
ar.L27 -0.1660 0.014 -11.811 0.000 -0.194 -0.138
ar.L28 -0.1113 0.012 -9.658 0.000 -0.134 -0.089
ar.L29 -0.0640 0.009 -7.123 0.000 -0.082 -0.046
ar.L30 -0.0026 0.005 -0.522 0.602 -0.012 0.007
ma.L1 -0.9626 0.002 -401.959 0.000 -0.967 -0.958
sigma2 0.0656 0.000 405.043 0.000 0.065 0.066
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 2061947.21
Prob(Q): 0.93 Prob(JB): 0.00
Heteroskedasticity (H): 25.40 Skew: 0.38
Prob(H) (two-sided): 0.00 Kurtosis: 53.74
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 44.033978
19224 43.658988
19225 43.521927
19226 43.609785
19227 43.599641
19228 43.543962
19229 43.450074
19230 43.335761
19231 43.282727
19232 43.190636
Name: predicted_mean, dtype: float64
lower price upper price
19223 43.531840 44.536116
19224 43.051440 44.266535
19225 42.743100 44.300755
19226 42.739416 44.480154
19227 42.583037 44.616245
19228 42.441955 44.645969
19229 42.205755 44.694393
19230 41.999444 44.672078
19231 41.804908 44.760546
19232 41.604964 44.776308
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 216.8891 FROTO
2021-12-27 10:00:00+03:00 225.8110 FROTO
2021-12-27 11:00:00+03:00 223.8482 FROTO
2021-12-27 12:00:00+03:00 221.4393 FROTO
2021-12-27 13:00:00+03:00 221.6177 FROTO
... ... ...
2024-01-11 14:00:00+03:00 776.5000 FROTO
2024-01-11 15:00:00+03:00 777.0000 FROTO
2024-01-11 16:00:00+03:00 783.0000 FROTO
2024-01-11 17:00:00+03:00 780.5000 FROTO
2024-01-11 18:00:00+03:00 778.0000 FROTO
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 216.8891
2021-12-27 07:00:00+00:00 225.8110
2021-12-27 08:00:00+00:00 223.8482
2021-12-27 09:00:00+00:00 221.4393
2021-12-27 10:00:00+00:00 221.6177
2021-12-27 11:00:00+00:00 217.9597
2021-12-27 12:00:00+00:00 218.5844
2021-12-27 13:00:00+00:00 218.6735
2021-12-27 14:00:00+00:00 218.4952
2021-12-27 15:00:00+00:00 217.7814
2021-12-28 06:00:00+00:00 219.9225
2021-12-28 07:00:00+00:00 219.5657
2021-12-28 08:00:00+00:00 216.8891
2021-12-28 09:00:00+00:00 216.5323
2021-12-28 10:00:00+00:00 217.5137
2021-12-28 11:00:00+00:00 214.3018
2021-12-28 12:00:00+00:00 216.6215
2021-12-28 13:00:00+00:00 214.4803
2021-12-28 14:00:00+00:00 208.9487
2021-12-28 15:00:00+00:00 212.4282
2021-12-29 06:00:00+00:00 215.8186
2021-12-29 07:00:00+00:00 213.5881
2021-12-29 08:00:00+00:00 216.1754
2021-12-29 09:00:00+00:00 217.6029
2021-12-29 10:00:00+00:00 216.4431
2021-12-29 11:00:00+00:00 216.2646
2021-12-29 12:00:00+00:00 217.9597
2021-12-29 13:00:00+00:00 218.0490
2021-12-29 14:00:00+00:00 217.7814
2021-12-29 15:00:00+00:00 216.7107
2021-12-30 06:00:00+00:00 218.1381
2021-12-30 07:00:00+00:00 216.2646
2021-12-30 08:00:00+00:00 217.7814
2021-12-30 09:00:00+00:00 216.6215
2021-12-30 10:00:00+00:00 215.9969
2021-12-30 11:00:00+00:00 212.6958
2021-12-30 12:00:00+00:00 214.3910
2021-12-30 13:00:00+00:00 213.7665
2021-12-30 14:00:00+00:00 214.9264
2021-12-30 15:00:00+00:00 213.5881
2021-12-31 06:00:00+00:00 214.1234
2021-12-31 07:00:00+00:00 214.3018
2021-12-31 08:00:00+00:00 216.1754
2021-12-31 09:00:00+00:00 216.1754
2021-12-31 10:00:00+00:00 215.5508
2021-12-31 11:00:00+00:00 216.3538
2021-12-31 12:00:00+00:00 213.7665
2021-12-31 13:00:00+00:00 215.2832
2021-12-31 14:00:00+00:00 211.6252
2021-12-31 15:00:00+00:00 212.4282
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 753.5
2024-01-10 10:00:00+00:00 751.5
2024-01-10 10:30:00+00:00 752.5
2024-01-10 11:00:00+00:00 754.0
2024-01-10 11:30:00+00:00 760.0
2024-01-10 12:00:00+00:00 762.0
2024-01-10 12:30:00+00:00 763.0
2024-01-10 13:00:00+00:00 771.5
2024-01-10 13:30:00+00:00 766.5
2024-01-10 14:00:00+00:00 768.5
2024-01-10 15:00:00+00:00 768.0
2024-01-11 06:00:00+00:00 772.0
2024-01-11 06:30:00+00:00 773.0
2024-01-11 07:00:00+00:00 768.5
2024-01-11 07:30:00+00:00 770.0
2024-01-11 08:00:00+00:00 769.5
2024-01-11 08:30:00+00:00 772.5
2024-01-11 09:00:00+00:00 774.0
2024-01-11 09:30:00+00:00 775.5
2024-01-11 10:00:00+00:00 776.5
2024-01-11 10:30:00+00:00 776.0
2024-01-11 11:00:00+00:00 776.5
2024-01-11 11:30:00+00:00 777.0
2024-01-11 12:00:00+00:00 777.0
2024-01-11 12:30:00+00:00 785.0
2024-01-11 13:00:00+00:00 783.0
2024-01-11 13:30:00+00:00 780.0
2024-01-11 14:00:00+00:00 780.5
2024-01-11 14:30:00+00:00 778.0
2024-01-11 15:00:00+00:00 778.0
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.2094
2018-01-02 11:00:00+03:00 0.3142
2018-01-02 12:00:00+03:00 0.1744
2018-01-02 13:00:00+03:00 -0.1395
2018-01-02 14:00:00+03:00 0.2791
...
2024-01-11 13:00:00+00:00 -2.0000
2024-01-11 13:30:00+00:00 -3.0000
2024-01-11 14:00:00+00:00 0.5000
2024-01-11 14:30:00+00:00 -2.5000
2024-01-11 15:00:00+00:00 0.0000
Name: price, Length: 19221, dtype: float64
ADF Statistic: -22.69384925598741
p-value: 0.0
Critical Values: {'1%': -3.4306910604704344, '5%': -2.86169073638432, '10%': -2.566850232920588}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19222
Model: ARIMA(30, 1, 1) Log Likelihood -58631.679
Date: Thu, 11 Jan 2024 AIC 117327.359
Time: 21:29:38 BIC 117578.999
Sample: 0 HQIC 117409.846
- 19222
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.7644 0.173 -4.417 0.000 -1.104 -0.425
ar.L2 -0.0801 0.151 -0.530 0.596 -0.377 0.216
ar.L3 -0.0528 0.030 -1.736 0.083 -0.112 0.007
ar.L4 0.0041 0.014 0.300 0.764 -0.023 0.031
ar.L5 -0.0689 0.006 -11.273 0.000 -0.081 -0.057
ar.L6 0.0059 0.014 0.432 0.666 -0.021 0.033
ar.L7 -0.0272 0.007 -4.017 0.000 -0.040 -0.014
ar.L8 0.0286 0.009 3.316 0.001 0.012 0.045
ar.L9 -0.0338 0.008 -4.203 0.000 -0.050 -0.018
ar.L10 0.0431 0.010 4.465 0.000 0.024 0.062
ar.L11 0.0028 0.010 0.295 0.768 -0.016 0.022
ar.L12 0.0165 0.007 2.263 0.024 0.002 0.031
ar.L13 0.0028 0.008 0.378 0.705 -0.012 0.018
ar.L14 -0.0119 0.007 -1.736 0.083 -0.025 0.002
ar.L15 -0.0212 0.007 -3.226 0.001 -0.034 -0.008
ar.L16 -0.1635 0.006 -25.227 0.000 -0.176 -0.151
ar.L17 -0.0532 0.029 -1.833 0.067 -0.110 0.004
ar.L18 0.1861 0.013 14.403 0.000 0.161 0.211
ar.L19 0.4670 0.031 15.008 0.000 0.406 0.528
ar.L20 -0.0341 0.084 -0.403 0.687 -0.200 0.131
ar.L21 -0.1984 0.007 -29.951 0.000 -0.211 -0.185
ar.L22 -0.0506 0.034 -1.468 0.142 -0.118 0.017
ar.L23 -0.0741 0.014 -5.353 0.000 -0.101 -0.047
ar.L24 -0.0036 0.016 -0.228 0.820 -0.034 0.027
ar.L25 -0.0581 0.007 -7.879 0.000 -0.073 -0.044
ar.L26 0.0126 0.013 0.989 0.323 -0.012 0.038
ar.L27 -0.0269 0.008 -3.570 0.000 -0.042 -0.012
ar.L28 0.0225 0.009 2.596 0.009 0.006 0.039
ar.L29 -0.0424 0.008 -5.254 0.000 -0.058 -0.027
ar.L30 0.0426 0.013 3.342 0.001 0.018 0.068
ma.L1 -0.1061 0.173 -0.613 0.540 -0.445 0.233
sigma2 26.1201 0.120 216.975 0.000 25.884 26.356
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 123484.84
Prob(Q): 0.93 Prob(JB): 0.00
Heteroskedasticity (H): 198.84 Skew: -0.10
Prob(H) (two-sided): 0.00 Kurtosis: 15.42
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19222 779.873428
19223 778.040535
19224 775.839863
19225 777.674855
19226 777.127177
19227 779.222758
19228 778.124548
19229 779.738985
19230 777.842032
19231 778.299331
Name: predicted_mean, dtype: float64
lower price upper price
19222 769.856475 789.890381
19223 767.939937 788.141134
19224 763.458765 788.220961
19225 764.970317 790.379393
19226 763.001140 791.253214
19227 764.793457 793.652059
19228 762.474962 793.774134
19229 763.801615 795.676356
19230 760.766077 794.917987
19231 760.993159 795.605504
price short_name
timestamp
2021-12-27 09:00:00+03:00 77.30 GUBRF
2021-12-27 10:00:00+03:00 81.00 GUBRF
2021-12-27 11:00:00+03:00 81.15 GUBRF
2021-12-27 12:00:00+03:00 82.35 GUBRF
2021-12-27 13:00:00+03:00 83.00 GUBRF
... ... ...
2024-01-11 14:00:00+03:00 141.90 GUBRF
2024-01-11 15:00:00+03:00 140.90 GUBRF
2024-01-11 16:00:00+03:00 141.10 GUBRF
2024-01-11 17:00:00+03:00 141.00 GUBRF
2024-01-11 18:00:00+03:00 141.10 GUBRF
[5103 rows x 2 columns]
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 77.30
2021-12-27 07:00:00+00:00 81.00
2021-12-27 08:00:00+00:00 81.15
2021-12-27 09:00:00+00:00 82.35
2021-12-27 10:00:00+00:00 83.00
2021-12-27 11:00:00+00:00 82.50
2021-12-27 12:00:00+00:00 83.15
2021-12-27 13:00:00+00:00 83.10
2021-12-27 14:00:00+00:00 83.15
2021-12-27 15:00:00+00:00 83.50
2021-12-28 06:00:00+00:00 85.60
2021-12-28 07:00:00+00:00 84.25
2021-12-28 08:00:00+00:00 82.85
2021-12-28 09:00:00+00:00 82.90
2021-12-28 10:00:00+00:00 83.50
2021-12-28 11:00:00+00:00 83.95
2021-12-28 12:00:00+00:00 83.60
2021-12-28 13:00:00+00:00 83.00
2021-12-28 14:00:00+00:00 81.85
2021-12-28 15:00:00+00:00 81.85
2021-12-29 06:00:00+00:00 81.90
2021-12-29 07:00:00+00:00 81.35
2021-12-29 08:00:00+00:00 82.00
2021-12-29 09:00:00+00:00 81.80
2021-12-29 10:00:00+00:00 82.00
2021-12-29 11:00:00+00:00 81.65
2021-12-29 12:00:00+00:00 81.80
2021-12-29 13:00:00+00:00 81.90
2021-12-29 14:00:00+00:00 81.40
2021-12-29 15:00:00+00:00 81.20
2021-12-30 06:00:00+00:00 82.05
2021-12-30 07:00:00+00:00 81.30
2021-12-30 08:00:00+00:00 81.25
2021-12-30 09:00:00+00:00 81.55
2021-12-30 10:00:00+00:00 82.05
2021-12-30 11:00:00+00:00 80.95
2021-12-30 12:00:00+00:00 80.95
2021-12-30 13:00:00+00:00 80.65
2021-12-30 14:00:00+00:00 78.65
2021-12-30 15:00:00+00:00 78.50
2021-12-31 06:00:00+00:00 79.15
2021-12-31 07:00:00+00:00 77.80
2021-12-31 08:00:00+00:00 77.60
2021-12-31 09:00:00+00:00 77.75
2021-12-31 10:00:00+00:00 77.35
2021-12-31 11:00:00+00:00 77.35
2021-12-31 12:00:00+00:00 77.20
2021-12-31 13:00:00+00:00 76.95
2021-12-31 14:00:00+00:00 76.70
2021-12-31 15:00:00+00:00 76.85
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 146.899994
2024-01-10 10:00:00+00:00 146.600000
2024-01-10 10:30:00+00:00 146.699997
2024-01-10 11:00:00+00:00 146.000000
2024-01-10 11:30:00+00:00 145.800003
2024-01-10 12:00:00+00:00 144.600000
2024-01-10 12:30:00+00:00 144.600006
2024-01-10 13:00:00+00:00 144.500000
2024-01-10 13:30:00+00:00 144.899994
2024-01-10 14:00:00+00:00 144.300000
2024-01-10 15:00:00+00:00 144.100000
2024-01-11 06:00:00+00:00 145.000000
2024-01-11 06:30:00+00:00 146.600006
2024-01-11 07:00:00+00:00 144.800000
2024-01-11 07:30:00+00:00 144.000000
2024-01-11 08:00:00+00:00 143.400000
2024-01-11 08:30:00+00:00 143.699997
2024-01-11 09:00:00+00:00 143.600000
2024-01-11 09:30:00+00:00 143.300003
2024-01-11 10:00:00+00:00 142.700000
2024-01-11 10:30:00+00:00 142.399994
2024-01-11 11:00:00+00:00 141.900000
2024-01-11 11:30:00+00:00 141.600006
2024-01-11 12:00:00+00:00 140.900000
2024-01-11 12:30:00+00:00 142.300003
2024-01-11 13:00:00+00:00 141.100000
2024-01-11 13:30:00+00:00 140.899994
2024-01-11 14:00:00+00:00 141.000000
2024-01-11 14:30:00+00:00 141.100006
2024-01-11 15:00:00+00:00 141.100000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 -0.010000
2018-01-02 11:00:00+03:00 0.030000
2018-01-02 12:00:00+03:00 0.030000
2018-01-02 13:00:00+03:00 0.010000
2018-01-02 14:00:00+03:00 0.030000
...
2024-01-11 13:00:00+00:00 -1.200003
2024-01-11 13:30:00+00:00 -0.200006
2024-01-11 14:00:00+00:00 0.100006
2024-01-11 14:30:00+00:00 0.100006
2024-01-11 15:00:00+00:00 -0.000006
Name: price, Length: 19212, dtype: float64
ADF Statistic: -18.2075741124874
p-value: 2.3972053326912787e-30
Critical Values: {'1%': -3.430691238445059, '5%': -2.861690815038142, '10%': -2.566850274786686}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19213
Model: ARIMA(30, 3, 1) Log Likelihood -33276.763
Date: Thu, 11 Jan 2024 AIC 66617.526
Time: 21:34:47 BIC 66869.148
Sample: 0 HQIC 66700.009
- 19213
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.9827 0.004 -274.075 0.000 -0.990 -0.976
ar.L2 -0.9423 0.005 -184.673 0.000 -0.952 -0.932
ar.L3 -0.9073 0.007 -137.582 0.000 -0.920 -0.894
ar.L4 -0.8842 0.008 -111.932 0.000 -0.900 -0.869
ar.L5 -0.8770 0.008 -105.052 0.000 -0.893 -0.861
ar.L6 -0.8649 0.009 -92.309 0.000 -0.883 -0.846
ar.L7 -0.8396 0.010 -82.356 0.000 -0.860 -0.820
ar.L8 -0.8088 0.011 -73.625 0.000 -0.830 -0.787
ar.L9 -0.7733 0.011 -67.301 0.000 -0.796 -0.751
ar.L10 -0.7636 0.012 -64.763 0.000 -0.787 -0.741
ar.L11 -0.7212 0.012 -58.499 0.000 -0.745 -0.697
ar.L12 -0.6781 0.012 -55.049 0.000 -0.702 -0.654
ar.L13 -0.6363 0.013 -50.574 0.000 -0.661 -0.612
ar.L14 -0.6017 0.012 -48.692 0.000 -0.626 -0.577
ar.L15 -0.5776 0.012 -47.885 0.000 -0.601 -0.554
ar.L16 -0.5558 0.012 -47.325 0.000 -0.579 -0.533
ar.L17 -0.5157 0.011 -45.232 0.000 -0.538 -0.493
ar.L18 -0.3429 0.012 -28.855 0.000 -0.366 -0.320
ar.L19 -0.2985 0.012 -25.738 0.000 -0.321 -0.276
ar.L20 -0.2976 0.011 -26.712 0.000 -0.319 -0.276
ar.L21 -0.2967 0.011 -27.977 0.000 -0.317 -0.276
ar.L22 -0.2744 0.010 -26.831 0.000 -0.294 -0.254
ar.L23 -0.2165 0.010 -22.292 0.000 -0.236 -0.197
ar.L24 -0.1812 0.009 -19.970 0.000 -0.199 -0.163
ar.L25 -0.1473 0.009 -17.152 0.000 -0.164 -0.130
ar.L26 -0.1106 0.008 -13.581 0.000 -0.127 -0.095
ar.L27 -0.0990 0.007 -13.321 0.000 -0.114 -0.084
ar.L28 -0.0799 0.006 -12.929 0.000 -0.092 -0.068
ar.L29 -0.0932 0.005 -19.626 0.000 -0.103 -0.084
ar.L30 -0.0586 0.003 -22.610 0.000 -0.064 -0.053
ma.L1 -1.0000 0.006 -181.721 0.000 -1.011 -0.989
sigma2 1.8695 0.009 212.873 0.000 1.852 1.887
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 3285170.73
Prob(Q): 0.92 Prob(JB): 0.00
Heteroskedasticity (H): 307.16 Skew: -1.92
Prob(H) (two-sided): 0.00 Kurtosis: 66.95
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19213 141.148688
19214 140.669771
19215 140.292672
19216 140.025225
19217 139.924207
19218 139.840951
19219 139.579083
19220 139.326880
19221 139.126220
19222 138.881193
Name: predicted_mean, dtype: float64
lower price upper price
19213 138.468773 143.828604
19214 136.846759 144.492782
19215 135.532861 145.052483
19216 134.434113 145.616337
19217 133.577602 146.270813
19218 132.807031 146.874870
19219 131.902417 147.255750
19220 131.028786 147.624974
19221 130.217149 148.035290
19222 129.363001 148.399385
price short_name
timestamp
2021-12-27 09:00:00+03:00 10.6645 GARAN
2021-12-27 10:00:00+03:00 10.7373 GARAN
2021-12-27 11:00:00+03:00 10.7282 GARAN
2021-12-27 12:00:00+03:00 10.7282 GARAN
2021-12-27 13:00:00+03:00 10.7191 GARAN
... ... ...
2024-01-11 14:00:00+03:00 66.0000 GARAN
2024-01-11 15:00:00+03:00 65.3500 GARAN
2024-01-11 16:00:00+03:00 65.5000 GARAN
2024-01-11 17:00:00+03:00 65.9000 GARAN
2024-01-11 18:00:00+03:00 65.6000 GARAN
[5104 rows x 2 columns]
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 10.6645
2021-12-27 07:00:00+00:00 10.7373
2021-12-27 08:00:00+00:00 10.7282
2021-12-27 09:00:00+00:00 10.7282
2021-12-27 10:00:00+00:00 10.7191
2021-12-27 11:00:00+00:00 10.6736
2021-12-27 12:00:00+00:00 10.6645
2021-12-27 13:00:00+00:00 10.6645
2021-12-27 14:00:00+00:00 10.6372
2021-12-27 15:00:00+00:00 10.6190
2021-12-28 06:00:00+00:00 10.6009
2021-12-28 07:00:00+00:00 10.5371
2021-12-28 08:00:00+00:00 10.5007
2021-12-28 09:00:00+00:00 10.4553
2021-12-28 10:00:00+00:00 10.4462
2021-12-28 11:00:00+00:00 10.4189
2021-12-28 12:00:00+00:00 10.4553
2021-12-28 13:00:00+00:00 10.4280
2021-12-28 14:00:00+00:00 10.2278
2021-12-28 15:00:00+00:00 10.2278
2021-12-29 06:00:00+00:00 10.1913
2021-12-29 07:00:00+00:00 10.2642
2021-12-29 08:00:00+00:00 10.4371
2021-12-29 09:00:00+00:00 10.4280
2021-12-29 10:00:00+00:00 10.4553
2021-12-29 11:00:00+00:00 10.4189
2021-12-29 12:00:00+00:00 10.4098
2021-12-29 13:00:00+00:00 10.4371
2021-12-29 14:00:00+00:00 10.3824
2021-12-29 15:00:00+00:00 10.4098
2021-12-30 06:00:00+00:00 10.4371
2021-12-30 07:00:00+00:00 10.5280
2021-12-30 08:00:00+00:00 10.5553
2021-12-30 09:00:00+00:00 10.5462
2021-12-30 10:00:00+00:00 10.5918
2021-12-30 11:00:00+00:00 10.5098
2021-12-30 12:00:00+00:00 10.5007
2021-12-30 13:00:00+00:00 10.5462
2021-12-30 14:00:00+00:00 10.4462
2021-12-30 15:00:00+00:00 10.4280
2021-12-31 06:00:00+00:00 10.3733
2021-12-31 07:00:00+00:00 10.3551
2021-12-31 08:00:00+00:00 10.3733
2021-12-31 09:00:00+00:00 10.3642
2021-12-31 10:00:00+00:00 10.3369
2021-12-31 11:00:00+00:00 10.3278
2021-12-31 12:00:00+00:00 10.2915
2021-12-31 13:00:00+00:00 10.2642
2021-12-31 14:00:00+00:00 10.2187
2021-12-31 15:00:00+00:00 10.2551
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 62.950001
2024-01-10 10:00:00+00:00 63.100000
2024-01-10 10:30:00+00:00 63.500000
2024-01-10 11:00:00+00:00 63.450000
2024-01-10 11:30:00+00:00 63.650002
2024-01-10 12:00:00+00:00 63.300000
2024-01-10 12:30:00+00:00 63.500000
2024-01-10 13:00:00+00:00 64.150000
2024-01-10 13:30:00+00:00 63.799999
2024-01-10 14:00:00+00:00 63.900000
2024-01-10 15:00:00+00:00 63.800000
2024-01-11 06:00:00+00:00 64.250000
2024-01-11 06:30:00+00:00 66.750000
2024-01-11 07:00:00+00:00 65.700000
2024-01-11 07:30:00+00:00 66.199997
2024-01-11 08:00:00+00:00 65.950000
2024-01-11 08:30:00+00:00 66.300003
2024-01-11 09:00:00+00:00 66.100000
2024-01-11 09:30:00+00:00 66.050003
2024-01-11 10:00:00+00:00 65.700000
2024-01-11 10:30:00+00:00 66.099998
2024-01-11 11:00:00+00:00 66.000000
2024-01-11 11:30:00+00:00 65.900002
2024-01-11 12:00:00+00:00 65.350000
2024-01-11 12:30:00+00:00 66.150002
2024-01-11 13:00:00+00:00 65.500000
2024-01-11 13:30:00+00:00 66.000000
2024-01-11 14:00:00+00:00 65.900000
2024-01-11 14:30:00+00:00 65.599998
2024-01-11 15:00:00+00:00 65.600000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.111000
2018-01-02 11:00:00+03:00 0.025700
2018-01-02 12:00:00+03:00 -0.017200
2018-01-02 13:00:00+03:00 0.008600
2018-01-02 14:00:00+03:00 0.008600
...
2024-01-11 13:00:00+00:00 -0.650002
2024-01-11 13:30:00+00:00 0.500000
2024-01-11 14:00:00+00:00 -0.100000
2024-01-11 14:30:00+00:00 -0.300002
2024-01-11 15:00:00+00:00 0.000002
Name: price, Length: 19223, dtype: float64
ADF Statistic: -20.50576228600906
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19224
Model: ARIMA(30, 1, 1) Log Likelihood -4954.666
Date: Thu, 11 Jan 2024 AIC 9973.331
Time: 21:37:31 BIC 10224.975
Sample: 0 HQIC 10055.819
- 19224
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.2520 0.027 -45.696 0.000 -1.306 -1.198
ar.L2 -0.3905 0.019 -20.938 0.000 -0.427 -0.354
ar.L3 -0.0640 0.007 -9.714 0.000 -0.077 -0.051
ar.L4 -0.0424 0.007 -5.656 0.000 -0.057 -0.028
ar.L5 -0.0288 0.008 -3.774 0.000 -0.044 -0.014
ar.L6 0.0031 0.008 0.390 0.697 -0.012 0.018
ar.L7 -0.0116 0.008 -1.427 0.154 -0.027 0.004
ar.L8 -0.0108 0.009 -1.265 0.206 -0.028 0.006
ar.L9 -0.0264 0.009 -3.004 0.003 -0.044 -0.009
ar.L10 0.0134 0.009 1.470 0.142 -0.004 0.031
ar.L11 -0.0141 0.008 -1.670 0.095 -0.031 0.002
ar.L12 -0.0179 0.008 -2.251 0.024 -0.034 -0.002
ar.L13 0.0072 0.008 0.920 0.357 -0.008 0.022
ar.L14 0.0046 0.008 0.605 0.545 -0.010 0.020
ar.L15 0.0203 0.007 2.732 0.006 0.006 0.035
ar.L16 -0.0860 0.007 -12.474 0.000 -0.099 -0.072
ar.L17 -0.1255 0.006 -20.971 0.000 -0.137 -0.114
ar.L18 0.2068 0.006 35.548 0.000 0.195 0.218
ar.L19 0.5322 0.008 67.104 0.000 0.517 0.548
ar.L20 0.1014 0.012 8.784 0.000 0.079 0.124
ar.L21 -0.1844 0.007 -26.633 0.000 -0.198 -0.171
ar.L22 -0.0745 0.008 -9.655 0.000 -0.090 -0.059
ar.L23 -0.0724 0.008 -9.575 0.000 -0.087 -0.058
ar.L24 -0.0401 0.008 -4.921 0.000 -0.056 -0.024
ar.L25 -0.0303 0.008 -3.699 0.000 -0.046 -0.014
ar.L26 0.0008 0.008 0.097 0.923 -0.016 0.017
ar.L27 -0.0160 0.008 -1.892 0.058 -0.033 0.001
ar.L28 -0.0085 0.009 -0.943 0.346 -0.026 0.009
ar.L29 0.0054 0.008 0.643 0.520 -0.011 0.022
ar.L30 0.0614 0.007 9.038 0.000 0.048 0.075
ma.L1 0.6058 0.028 21.928 0.000 0.552 0.660
sigma2 0.0961 0.000 243.936 0.000 0.095 0.097
===================================================================================
Ljung-Box (L1) (Q): 1.00 Jarque-Bera (JB): 193049.00
Prob(Q): 0.32 Prob(JB): 0.00
Heteroskedasticity (H): 41.43 Skew: -0.06
Prob(H) (two-sided): 0.00 Kurtosis: 18.52
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224 66.417542
19225 66.535853
19226 65.824443
19227 66.199842
19228 65.984873
19229 66.038206
19230 65.978039
19231 65.816572
19232 65.928920
19233 66.039502
Name: predicted_mean, dtype: float64
lower price upper price
19224 65.809818 67.025266
19225 65.891214 67.180492
19226 65.027024 66.621862
19227 65.359428 67.040256
19228 65.044980 66.924767
19229 65.054975 67.021436
19230 64.913175 67.042903
19231 64.712097 66.921047
19232 64.755054 67.102785
19233 64.830327 67.248676
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 8.8236 KRDMD
2021-12-27 10:00:00+03:00 9.1310 KRDMD
2021-12-27 11:00:00+03:00 9.0565 KRDMD
2021-12-27 12:00:00+03:00 9.0472 KRDMD
2021-12-27 13:00:00+03:00 9.0006 KRDMD
... ... ...
2024-01-11 14:00:00+03:00 25.8200 KRDMD
2024-01-11 15:00:00+03:00 25.5200 KRDMD
2024-01-11 16:00:00+03:00 25.6000 KRDMD
2024-01-11 17:00:00+03:00 25.7400 KRDMD
2024-01-11 18:00:00+03:00 25.8600 KRDMD
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 8.8236
2021-12-27 07:00:00+00:00 9.1310
2021-12-27 08:00:00+00:00 9.0565
2021-12-27 09:00:00+00:00 9.0472
2021-12-27 10:00:00+00:00 9.0006
2021-12-27 11:00:00+00:00 8.9075
2021-12-27 12:00:00+00:00 8.8329
2021-12-27 13:00:00+00:00 8.8329
2021-12-27 14:00:00+00:00 8.7489
2021-12-27 15:00:00+00:00 8.7397
2021-12-28 06:00:00+00:00 8.8702
2021-12-28 07:00:00+00:00 8.8049
2021-12-28 08:00:00+00:00 8.7770
2021-12-28 09:00:00+00:00 8.7584
2021-12-28 10:00:00+00:00 8.7304
2021-12-28 11:00:00+00:00 8.5813
2021-12-28 12:00:00+00:00 8.6373
2021-12-28 13:00:00+00:00 8.5627
2021-12-28 14:00:00+00:00 8.3857
2021-12-28 15:00:00+00:00 8.3764
2021-12-29 06:00:00+00:00 8.3671
2021-12-29 07:00:00+00:00 8.3483
2021-12-29 08:00:00+00:00 8.5721
2021-12-29 09:00:00+00:00 8.5999
2021-12-29 10:00:00+00:00 8.6559
2021-12-29 11:00:00+00:00 8.6373
2021-12-29 12:00:00+00:00 8.6186
2021-12-29 13:00:00+00:00 8.7025
2021-12-29 14:00:00+00:00 8.7118
2021-12-29 15:00:00+00:00 8.7304
2021-12-30 06:00:00+00:00 8.8889
2021-12-30 07:00:00+00:00 8.7863
2021-12-30 08:00:00+00:00 8.7211
2021-12-30 09:00:00+00:00 8.6279
2021-12-30 10:00:00+00:00 8.6092
2021-12-30 11:00:00+00:00 8.4509
2021-12-30 12:00:00+00:00 8.4975
2021-12-30 13:00:00+00:00 8.5721
2021-12-30 14:00:00+00:00 8.4881
2021-12-30 15:00:00+00:00 8.4788
2021-12-31 06:00:00+00:00 8.4788
2021-12-31 07:00:00+00:00 8.5068
2021-12-31 08:00:00+00:00 8.5721
2021-12-31 09:00:00+00:00 8.6092
2021-12-31 10:00:00+00:00 8.5348
2021-12-31 11:00:00+00:00 8.5534
2021-12-31 12:00:00+00:00 8.5440
2021-12-31 13:00:00+00:00 8.6092
2021-12-31 14:00:00+00:00 8.5254
2021-12-31 15:00:00+00:00 8.5906
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 25.360001
2024-01-10 10:00:00+00:00 25.340000
2024-01-10 10:30:00+00:00 25.379999
2024-01-10 11:00:00+00:00 25.320000
2024-01-10 11:30:00+00:00 25.240000
2024-01-10 12:00:00+00:00 25.160000
2024-01-10 12:30:00+00:00 25.360001
2024-01-10 13:00:00+00:00 25.480000
2024-01-10 13:30:00+00:00 25.459999
2024-01-10 14:00:00+00:00 25.460000
2024-01-10 15:00:00+00:00 25.420000
2024-01-11 06:00:00+00:00 25.440000
2024-01-11 06:30:00+00:00 25.719999
2024-01-11 07:00:00+00:00 25.600000
2024-01-11 07:30:00+00:00 25.540001
2024-01-11 08:00:00+00:00 25.660000
2024-01-11 08:30:00+00:00 25.719999
2024-01-11 09:00:00+00:00 25.720000
2024-01-11 09:30:00+00:00 25.680000
2024-01-11 10:00:00+00:00 25.700000
2024-01-11 10:30:00+00:00 25.680000
2024-01-11 11:00:00+00:00 25.820000
2024-01-11 11:30:00+00:00 25.719999
2024-01-11 12:00:00+00:00 25.520000
2024-01-11 12:30:00+00:00 25.799999
2024-01-11 13:00:00+00:00 25.600000
2024-01-11 13:30:00+00:00 25.680000
2024-01-11 14:00:00+00:00 25.740000
2024-01-11 14:30:00+00:00 25.860001
2024-01-11 15:00:00+00:00 25.860000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 -1.660000e-02
2018-01-02 11:00:00+03:00 4.150000e-02
2018-01-02 12:00:00+03:00 -2.490000e-02
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 8.300000e-03
...
2024-01-11 13:00:00+00:00 -1.999992e-01
2024-01-11 13:30:00+00:00 8.000031e-02
2024-01-11 14:00:00+00:00 5.999969e-02
2024-01-11 14:30:00+00:00 1.200006e-01
2024-01-11 15:00:00+00:00 -6.103516e-07
Name: price, Length: 19223, dtype: float64
ADF Statistic: -20.545140810318422
p-value: 0.0
Critical Values: {'1%': -3.4306910426831823, '5%': -2.86169072852345, '10%': -2.5668502287363797}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19224
Model: ARIMA(30, 3, 1) Log Likelihood 8618.516
Date: Thu, 11 Jan 2024 AIC -17173.031
Time: 21:40:30 BIC -16921.391
Sample: 0 HQIC -17090.544
- 19224
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.4046 0.004 -336.622 0.000 -1.413 -1.396
ar.L2 -1.2413 0.009 -142.237 0.000 -1.258 -1.224
ar.L3 -1.3032 0.012 -107.087 0.000 -1.327 -1.279
ar.L4 -1.1937 0.015 -78.192 0.000 -1.224 -1.164
ar.L5 -1.1650 0.018 -65.593 0.000 -1.200 -1.130
ar.L6 -1.0798 0.020 -54.172 0.000 -1.119 -1.041
ar.L7 -1.0365 0.022 -47.893 0.000 -1.079 -0.994
ar.L8 -0.9501 0.023 -41.993 0.000 -0.994 -0.906
ar.L9 -0.9275 0.023 -39.608 0.000 -0.973 -0.882
ar.L10 -0.8871 0.024 -36.335 0.000 -0.935 -0.839
ar.L11 -0.8606 0.025 -34.093 0.000 -0.910 -0.811
ar.L12 -0.8613 0.026 -33.241 0.000 -0.912 -0.810
ar.L13 -0.7716 0.026 -29.415 0.000 -0.823 -0.720
ar.L14 -0.7490 0.026 -28.464 0.000 -0.801 -0.697
ar.L15 -0.7171 0.026 -27.147 0.000 -0.769 -0.665
ar.L16 -0.7547 0.026 -28.891 0.000 -0.806 -0.703
ar.L17 -0.8116 0.026 -31.584 0.000 -0.862 -0.761
ar.L18 -0.4588 0.026 -17.854 0.000 -0.509 -0.408
ar.L19 -0.2520 0.025 -10.120 0.000 -0.301 -0.203
ar.L20 -0.4038 0.024 -16.860 0.000 -0.451 -0.357
ar.L21 -0.3635 0.023 -15.552 0.000 -0.409 -0.318
ar.L22 -0.3596 0.022 -16.067 0.000 -0.403 -0.316
ar.L23 -0.3238 0.022 -14.840 0.000 -0.367 -0.281
ar.L24 -0.2615 0.020 -13.026 0.000 -0.301 -0.222
ar.L25 -0.2370 0.019 -12.739 0.000 -0.273 -0.201
ar.L26 -0.1714 0.017 -10.075 0.000 -0.205 -0.138
ar.L27 -0.1441 0.015 -9.356 0.000 -0.174 -0.114
ar.L28 -0.0515 0.013 -4.023 0.000 -0.077 -0.026
ar.L29 -0.0410 0.010 -4.163 0.000 -0.060 -0.022
ar.L30 0.0559 0.006 9.099 0.000 0.044 0.068
ma.L1 -0.9712 0.003 -359.666 0.000 -0.977 -0.966
sigma2 0.0242 0.000 236.896 0.000 0.024 0.024
===================================================================================
Ljung-Box (L1) (Q): 3.31 Jarque-Bera (JB): 249797.70
Prob(Q): 0.07 Prob(JB): 0.00
Heteroskedasticity (H): 50.60 Skew: 0.26
Prob(H) (two-sided): 0.00 Kurtosis: 20.65
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224 26.003483
19225 25.945138
19226 25.943732
19227 25.970318
19228 26.084576
19229 26.063838
19230 26.114262
19231 26.113155
19232 26.143461
19233 26.213951
Name: predicted_mean, dtype: float64
lower price upper price
19224 25.698528 26.308438
19225 25.585649 26.304627
19226 25.478508 26.408955
19227 25.453973 26.486664
19228 25.477502 26.691651
19229 25.400418 26.727258
19230 25.363525 26.865000
19231 25.300716 26.925595
19232 25.241621 27.045301
19233 25.247232 27.180671
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 28.3315 KCHOL
2021-12-27 10:00:00+03:00 28.7712 KCHOL
2021-12-27 11:00:00+03:00 28.6565 KCHOL
2021-12-27 12:00:00+03:00 28.6375 KCHOL
2021-12-27 13:00:00+03:00 28.6565 KCHOL
... ... ...
2024-01-11 14:00:00+03:00 150.0000 KCHOL
2024-01-11 15:00:00+03:00 150.8000 KCHOL
2024-01-11 16:00:00+03:00 152.9000 KCHOL
2024-01-11 17:00:00+03:00 153.2000 KCHOL
2024-01-11 18:00:00+03:00 153.2000 KCHOL
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 28.3315
2021-12-27 07:00:00+00:00 28.7712
2021-12-27 08:00:00+00:00 28.6565
2021-12-27 09:00:00+00:00 28.6375
2021-12-27 10:00:00+00:00 28.6565
2021-12-27 11:00:00+00:00 28.2360
2021-12-27 12:00:00+00:00 28.4272
2021-12-27 13:00:00+00:00 28.4654
2021-12-27 14:00:00+00:00 28.1977
2021-12-27 15:00:00+00:00 28.1404
2021-12-28 06:00:00+00:00 28.2550
2021-12-28 07:00:00+00:00 28.0639
2021-12-28 08:00:00+00:00 27.7389
2021-12-28 09:00:00+00:00 27.6815
2021-12-28 10:00:00+00:00 27.7198
2021-12-28 11:00:00+00:00 27.5477
2021-12-28 12:00:00+00:00 27.6433
2021-12-28 13:00:00+00:00 27.3757
2021-12-28 14:00:00+00:00 27.1845
2021-12-28 15:00:00+00:00 27.4330
2021-12-29 06:00:00+00:00 27.3184
2021-12-29 07:00:00+00:00 27.0316
2021-12-29 08:00:00+00:00 27.6051
2021-12-29 09:00:00+00:00 27.7198
2021-12-29 10:00:00+00:00 27.7198
2021-12-29 11:00:00+00:00 27.7772
2021-12-29 12:00:00+00:00 28.0257
2021-12-29 13:00:00+00:00 28.2550
2021-12-29 14:00:00+00:00 28.1595
2021-12-29 15:00:00+00:00 28.1595
2021-12-30 06:00:00+00:00 28.3698
2021-12-30 07:00:00+00:00 28.0065
2021-12-30 08:00:00+00:00 28.1977
2021-12-30 09:00:00+00:00 28.1977
2021-12-30 10:00:00+00:00 28.1022
2021-12-30 11:00:00+00:00 27.7963
2021-12-30 12:00:00+00:00 27.8154
2021-12-30 13:00:00+00:00 27.8537
2021-12-30 14:00:00+00:00 27.5477
2021-12-30 15:00:00+00:00 27.4330
2021-12-31 06:00:00+00:00 27.4522
2021-12-31 07:00:00+00:00 27.4139
2021-12-31 08:00:00+00:00 27.7580
2021-12-31 09:00:00+00:00 27.7963
2021-12-31 10:00:00+00:00 27.3757
2021-12-31 11:00:00+00:00 27.4330
2021-12-31 12:00:00+00:00 27.3565
2021-12-31 13:00:00+00:00 27.3565
2021-12-31 14:00:00+00:00 27.1272
2021-12-31 15:00:00+00:00 27.1272
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 144.699997
2024-01-10 10:00:00+00:00 144.400000
2024-01-10 10:30:00+00:00 144.600006
2024-01-10 11:00:00+00:00 144.800000
2024-01-10 11:30:00+00:00 144.800003
2024-01-10 12:00:00+00:00 144.600000
2024-01-10 12:30:00+00:00 146.300003
2024-01-10 13:00:00+00:00 146.900000
2024-01-10 13:30:00+00:00 146.800003
2024-01-10 14:00:00+00:00 147.600000
2024-01-10 15:00:00+00:00 147.100000
2024-01-11 06:00:00+00:00 147.400000
2024-01-11 06:30:00+00:00 147.500000
2024-01-11 07:00:00+00:00 146.900000
2024-01-11 07:30:00+00:00 147.100006
2024-01-11 08:00:00+00:00 147.100000
2024-01-11 08:30:00+00:00 149.300003
2024-01-11 09:00:00+00:00 150.100000
2024-01-11 09:30:00+00:00 150.199997
2024-01-11 10:00:00+00:00 150.200000
2024-01-11 10:30:00+00:00 150.100006
2024-01-11 11:00:00+00:00 150.000000
2024-01-11 11:30:00+00:00 150.899994
2024-01-11 12:00:00+00:00 150.800000
2024-01-11 12:30:00+00:00 152.600006
2024-01-11 13:00:00+00:00 152.900000
2024-01-11 13:30:00+00:00 152.699997
2024-01-11 14:00:00+00:00 153.200000
2024-01-11 14:30:00+00:00 153.199997
2024-01-11 15:00:00+00:00 153.200000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 -0.026200
2018-01-02 11:00:00+03:00 0.252400
2018-01-02 12:00:00+03:00 -0.087000
2018-01-02 13:00:00+03:00 0.078200
2018-01-02 14:00:00+03:00 0.130500
...
2024-01-11 13:00:00+00:00 0.299994
2024-01-11 13:30:00+00:00 -0.200003
2024-01-11 14:00:00+00:00 0.500003
2024-01-11 14:30:00+00:00 -0.000003
2024-01-11 15:00:00+00:00 0.000003
Name: price, Length: 19222, dtype: float64
ADF Statistic: -21.73931530065581
p-value: 0.0
Critical Values: {'1%': -3.4306910604704344, '5%': -2.86169073638432, '10%': -2.566850232920588}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 3, 1) Log Likelihood -13379.376
Date: Thu, 11 Jan 2024 AIC 26822.752
Time: 21:45:41 BIC 27074.391
Sample: 0 HQIC 26905.239
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.2840 0.003 -380.301 0.000 -1.291 -1.277
ar.L2 -1.1421 0.005 -209.057 0.000 -1.153 -1.131
ar.L3 -1.2198 0.007 -168.596 0.000 -1.234 -1.206
ar.L4 -1.0992 0.009 -126.801 0.000 -1.116 -1.082
ar.L5 -1.1005 0.010 -111.528 0.000 -1.120 -1.081
ar.L6 -0.9938 0.011 -89.698 0.000 -1.016 -0.972
ar.L7 -0.9809 0.012 -80.628 0.000 -1.005 -0.957
ar.L8 -0.9312 0.013 -74.047 0.000 -0.956 -0.907
ar.L9 -0.9241 0.013 -71.888 0.000 -0.949 -0.899
ar.L10 -0.8305 0.013 -62.355 0.000 -0.857 -0.804
ar.L11 -0.7711 0.013 -57.628 0.000 -0.797 -0.745
ar.L12 -0.7363 0.013 -56.129 0.000 -0.762 -0.711
ar.L13 -0.6896 0.013 -53.057 0.000 -0.715 -0.664
ar.L14 -0.6714 0.013 -52.839 0.000 -0.696 -0.647
ar.L15 -0.6111 0.012 -49.267 0.000 -0.635 -0.587
ar.L16 -0.5982 0.012 -50.102 0.000 -0.622 -0.575
ar.L17 -0.5834 0.011 -51.638 0.000 -0.606 -0.561
ar.L18 -0.4119 0.011 -37.965 0.000 -0.433 -0.391
ar.L19 -0.3230 0.011 -29.736 0.000 -0.344 -0.302
ar.L20 -0.3872 0.010 -37.365 0.000 -0.407 -0.367
ar.L21 -0.3573 0.009 -37.803 0.000 -0.376 -0.339
ar.L22 -0.3529 0.009 -40.124 0.000 -0.370 -0.336
ar.L23 -0.3146 0.009 -36.404 0.000 -0.332 -0.298
ar.L24 -0.2586 0.007 -35.239 0.000 -0.273 -0.244
ar.L25 -0.2140 0.006 -36.515 0.000 -0.225 -0.203
ar.L26 -0.1037 0.004 -25.008 0.000 -0.112 -0.096
ar.L27 -0.0837 0.003 -26.720 0.000 -0.090 -0.078
ar.L28 -0.0288 0.005 -6.034 0.000 -0.038 -0.019
ar.L29 -0.0571 0.005 -11.855 0.000 -0.067 -0.048
ar.L30 0.0315 0.004 8.174 0.000 0.024 0.039
ma.L1 -0.9998 0.001 -680.739 0.000 -1.003 -0.997
sigma2 0.2356 0.001 271.802 0.000 0.234 0.237
===================================================================================
Ljung-Box (L1) (Q): 0.22 Jarque-Bera (JB): 403118.56
Prob(Q): 0.64 Prob(JB): 0.00
Heteroskedasticity (H): 52.23 Skew: -0.10
Prob(H) (two-sided): 0.00 Kurtosis: 25.44
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 153.669634
19224 153.731560
19225 154.243594
19226 154.417231
19227 155.152393
19228 155.514859
19229 155.724208
19230 155.943472
19231 156.057993
19232 156.363806
Name: predicted_mean, dtype: float64
lower price upper price
19223 152.718388 154.620880
19224 152.561533 154.901588
19225 152.771685 155.715502
19226 152.778294 156.056168
19227 153.265153 157.039633
19228 153.465040 157.564677
19229 153.439047 158.009368
19230 153.495781 158.391162
19231 153.395452 158.720535
19232 153.545049 159.182563
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 4.9696 KOZAL
2021-12-27 10:00:00+03:00 5.0971 KOZAL
2021-12-27 11:00:00+03:00 5.0716 KOZAL
2021-12-27 12:00:00+03:00 5.0504 KOZAL
2021-12-27 13:00:00+03:00 5.0716 KOZAL
... ... ...
2024-01-11 14:00:00+03:00 20.1800 KOZAL
2024-01-11 15:00:00+03:00 19.9400 KOZAL
2024-01-11 16:00:00+03:00 19.9300 KOZAL
2024-01-11 17:00:00+03:00 19.9300 KOZAL
2024-01-11 18:00:00+03:00 19.9100 KOZAL
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 4.9696
2021-12-27 07:00:00+00:00 5.0971
2021-12-27 08:00:00+00:00 5.0716
2021-12-27 09:00:00+00:00 5.0504
2021-12-27 10:00:00+00:00 5.0716
2021-12-27 11:00:00+00:00 5.0334
2021-12-27 12:00:00+00:00 5.0334
2021-12-27 13:00:00+00:00 5.0504
2021-12-27 14:00:00+00:00 4.9952
2021-12-27 15:00:00+00:00 5.0121
2021-12-28 06:00:00+00:00 5.0802
2021-12-28 07:00:00+00:00 5.1439
2021-12-28 08:00:00+00:00 5.0887
2021-12-28 09:00:00+00:00 5.0887
2021-12-28 10:00:00+00:00 5.0971
2021-12-28 11:00:00+00:00 5.0547
2021-12-28 12:00:00+00:00 5.1355
2021-12-28 13:00:00+00:00 5.1057
2021-12-28 14:00:00+00:00 5.0716
2021-12-28 15:00:00+00:00 5.0844
2021-12-29 06:00:00+00:00 5.0844
2021-12-29 07:00:00+00:00 4.9994
2021-12-29 08:00:00+00:00 5.0207
2021-12-29 09:00:00+00:00 5.0249
2021-12-29 10:00:00+00:00 5.0207
2021-12-29 11:00:00+00:00 5.0207
2021-12-29 12:00:00+00:00 5.0376
2021-12-29 13:00:00+00:00 5.0674
2021-12-29 14:00:00+00:00 5.0419
2021-12-29 15:00:00+00:00 5.0504
2021-12-30 06:00:00+00:00 5.1227
2021-12-30 07:00:00+00:00 5.0547
2021-12-30 08:00:00+00:00 5.0589
2021-12-30 09:00:00+00:00 5.0547
2021-12-30 10:00:00+00:00 5.0461
2021-12-30 11:00:00+00:00 4.9696
2021-12-30 12:00:00+00:00 4.9696
2021-12-30 13:00:00+00:00 4.9866
2021-12-30 14:00:00+00:00 4.9399
2021-12-30 15:00:00+00:00 4.9101
2021-12-31 06:00:00+00:00 4.9314
2021-12-31 07:00:00+00:00 4.9399
2021-12-31 08:00:00+00:00 4.9739
2021-12-31 09:00:00+00:00 4.9866
2021-12-31 10:00:00+00:00 4.9568
2021-12-31 11:00:00+00:00 4.9739
2021-12-31 12:00:00+00:00 4.9526
2021-12-31 13:00:00+00:00 4.9612
2021-12-31 14:00:00+00:00 4.9228
2021-12-31 15:00:00+00:00 4.9186
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 19.530001
2024-01-10 10:00:00+00:00 19.540000
2024-01-10 10:30:00+00:00 19.530001
2024-01-10 11:00:00+00:00 19.560000
2024-01-10 11:30:00+00:00 19.549999
2024-01-10 12:00:00+00:00 19.410000
2024-01-10 12:30:00+00:00 19.549999
2024-01-10 13:00:00+00:00 19.640000
2024-01-10 13:30:00+00:00 19.680000
2024-01-10 14:00:00+00:00 19.650000
2024-01-10 15:00:00+00:00 19.600000
2024-01-11 06:00:00+00:00 19.690000
2024-01-11 06:30:00+00:00 19.990000
2024-01-11 07:00:00+00:00 19.940000
2024-01-11 07:30:00+00:00 20.080000
2024-01-11 08:00:00+00:00 20.060000
2024-01-11 08:30:00+00:00 20.080000
2024-01-11 09:00:00+00:00 20.060000
2024-01-11 09:30:00+00:00 20.059999
2024-01-11 10:00:00+00:00 20.140000
2024-01-11 10:30:00+00:00 20.040001
2024-01-11 11:00:00+00:00 20.180000
2024-01-11 11:30:00+00:00 20.059999
2024-01-11 12:00:00+00:00 19.940000
2024-01-11 12:30:00+00:00 20.120001
2024-01-11 13:00:00+00:00 19.930000
2024-01-11 13:30:00+00:00 19.910000
2024-01-11 14:00:00+00:00 19.930000
2024-01-11 14:30:00+00:00 19.910000
2024-01-11 15:00:00+00:00 19.910000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 2.040000e-02
2018-01-02 11:00:00+03:00 -5.100000e-03
2018-01-02 12:00:00+03:00 -3.400000e-03
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 1.440000e-02
...
2024-01-11 13:00:00+00:00 -1.900008e-01
2024-01-11 13:30:00+00:00 -2.000015e-02
2024-01-11 14:00:00+00:00 2.000015e-02
2024-01-11 14:30:00+00:00 -2.000015e-02
2024-01-11 15:00:00+00:00 1.525879e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -19.90380801842556
p-value: 0.0
Critical Values: {'1%': -3.4306910604704344, '5%': -2.86169073638432, '10%': -2.566850232920588}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 1, 1) Log Likelihood -25713.522
Date: Thu, 11 Jan 2024 AIC 51491.043
Time: 21:48:15 BIC 51742.685
Sample: 0 HQIC 51573.531
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.6068 0.031 -19.786 0.000 -0.667 -0.547
ar.L2 0.3416 0.045 7.550 0.000 0.253 0.430
ar.L3 0.1720 0.029 5.963 0.000 0.115 0.229
ar.L4 0.0326 0.019 1.734 0.083 -0.004 0.070
ar.L5 -0.0080 0.015 -0.523 0.601 -0.038 0.022
ar.L6 0.0396 0.015 2.734 0.006 0.011 0.068
ar.L7 -0.0124 0.011 -1.121 0.262 -0.034 0.009
ar.L8 0.0011 0.010 0.109 0.913 -0.019 0.021
ar.L9 -0.0227 0.010 -2.196 0.028 -0.043 -0.002
ar.L10 0.0100 0.010 0.973 0.330 -0.010 0.030
ar.L11 -0.0297 0.008 -3.848 0.000 -0.045 -0.015
ar.L12 -0.0997 0.008 -12.564 0.000 -0.115 -0.084
ar.L13 -0.0548 0.010 -5.579 0.000 -0.074 -0.036
ar.L14 -0.0697 0.010 -7.237 0.000 -0.089 -0.051
ar.L15 -0.0197 0.010 -1.970 0.049 -0.039 -0.000
ar.L16 -0.1132 0.009 -11.918 0.000 -0.132 -0.095
ar.L17 0.0120 0.011 1.047 0.295 -0.010 0.035
ar.L18 0.7733 0.009 83.123 0.000 0.755 0.792
ar.L19 0.5639 0.016 34.689 0.000 0.532 0.596
ar.L20 -0.3612 0.032 -11.467 0.000 -0.423 -0.299
ar.L21 -0.2728 0.016 -16.592 0.000 -0.305 -0.241
ar.L22 -0.0518 0.006 -8.530 0.000 -0.064 -0.040
ar.L23 -0.0647 0.004 -16.134 0.000 -0.073 -0.057
ar.L24 -0.0522 0.004 -13.836 0.000 -0.060 -0.045
ar.L25 -0.0632 0.004 -16.450 0.000 -0.071 -0.056
ar.L26 -0.0179 0.006 -3.027 0.002 -0.029 -0.006
ar.L27 -0.0496 0.007 -7.099 0.000 -0.063 -0.036
ar.L28 -0.0266 0.007 -3.660 0.000 -0.041 -0.012
ar.L29 -0.0468 0.008 -5.989 0.000 -0.062 -0.031
ar.L30 0.0716 0.008 8.756 0.000 0.056 0.088
ma.L1 -0.8592 0.031 -27.744 0.000 -0.920 -0.798
sigma2 0.8525 0.002 434.362 0.000 0.849 0.856
===================================================================================
Ljung-Box (L1) (Q): 0.34 Jarque-Bera (JB): 31488361.51
Prob(Q): 0.56 Prob(JB): 0.00
Heteroskedasticity (H): 736.06 Skew: 5.35
Prob(H) (two-sided): 0.00 Kurtosis: 200.99
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 20.183358
19224 20.109466
19225 20.195960
19226 20.148100
19227 20.168284
19228 20.071207
19229 20.123237
19230 20.102934
19231 20.103833
19232 20.112971
Name: predicted_mean, dtype: float64
lower price upper price
19223 18.373687 21.993028
19224 18.112949 22.105983
19225 17.766214 22.625706
19226 17.654155 22.642045
19227 17.487597 22.848971
19228 17.369366 22.773049
19229 17.299027 22.947447
19230 17.265318 22.940550
19231 17.169092 23.038573
19232 17.167400 23.058542
price short_name
timestamp
2021-12-27 09:00:00+03:00 17.76 KOZAA
2021-12-27 10:00:00+03:00 18.25 KOZAA
2021-12-27 11:00:00+03:00 18.04 KOZAA
2021-12-27 12:00:00+03:00 17.92 KOZAA
2021-12-27 13:00:00+03:00 18.14 KOZAA
... ... ...
2024-01-11 14:00:00+03:00 43.94 KOZAA
2024-01-11 15:00:00+03:00 43.58 KOZAA
2024-01-11 16:00:00+03:00 43.60 KOZAA
2024-01-11 17:00:00+03:00 43.62 KOZAA
2024-01-11 18:00:00+03:00 43.54 KOZAA
[5104 rows x 2 columns]
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 17.76
2021-12-27 07:00:00+00:00 18.25
2021-12-27 08:00:00+00:00 18.04
2021-12-27 09:00:00+00:00 17.92
2021-12-27 10:00:00+00:00 18.14
2021-12-27 11:00:00+00:00 18.11
2021-12-27 12:00:00+00:00 18.00
2021-12-27 13:00:00+00:00 18.04
2021-12-27 14:00:00+00:00 17.93
2021-12-27 15:00:00+00:00 17.94
2021-12-28 06:00:00+00:00 18.16
2021-12-28 07:00:00+00:00 18.33
2021-12-28 08:00:00+00:00 18.06
2021-12-28 09:00:00+00:00 18.06
2021-12-28 10:00:00+00:00 18.04
2021-12-28 11:00:00+00:00 17.91
2021-12-28 12:00:00+00:00 18.20
2021-12-28 13:00:00+00:00 18.00
2021-12-28 14:00:00+00:00 17.72
2021-12-28 15:00:00+00:00 17.77
2021-12-29 06:00:00+00:00 17.72
2021-12-29 07:00:00+00:00 17.57
2021-12-29 08:00:00+00:00 17.77
2021-12-29 09:00:00+00:00 17.77
2021-12-29 10:00:00+00:00 17.87
2021-12-29 11:00:00+00:00 17.84
2021-12-29 12:00:00+00:00 17.99
2021-12-29 13:00:00+00:00 18.11
2021-12-29 14:00:00+00:00 17.91
2021-12-29 15:00:00+00:00 17.91
2021-12-30 06:00:00+00:00 18.26
2021-12-30 07:00:00+00:00 18.05
2021-12-30 08:00:00+00:00 18.17
2021-12-30 09:00:00+00:00 18.13
2021-12-30 10:00:00+00:00 18.15
2021-12-30 11:00:00+00:00 17.86
2021-12-30 12:00:00+00:00 17.80
2021-12-30 13:00:00+00:00 17.93
2021-12-30 14:00:00+00:00 17.84
2021-12-30 15:00:00+00:00 17.74
2021-12-31 06:00:00+00:00 17.80
2021-12-31 07:00:00+00:00 17.74
2021-12-31 08:00:00+00:00 17.96
2021-12-31 09:00:00+00:00 18.03
2021-12-31 10:00:00+00:00 17.96
2021-12-31 11:00:00+00:00 18.04
2021-12-31 12:00:00+00:00 17.88
2021-12-31 13:00:00+00:00 18.05
2021-12-31 14:00:00+00:00 17.94
2021-12-31 15:00:00+00:00 18.02
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 42.119999
2024-01-10 10:00:00+00:00 42.160000
2024-01-10 10:30:00+00:00 42.160000
2024-01-10 11:00:00+00:00 42.340000
2024-01-10 11:30:00+00:00 42.400002
2024-01-10 12:00:00+00:00 42.000000
2024-01-10 12:30:00+00:00 42.380001
2024-01-10 13:00:00+00:00 42.600000
2024-01-10 13:30:00+00:00 42.700001
2024-01-10 14:00:00+00:00 42.660000
2024-01-10 15:00:00+00:00 42.660000
2024-01-11 06:00:00+00:00 42.720000
2024-01-11 06:30:00+00:00 43.400002
2024-01-11 07:00:00+00:00 43.500000
2024-01-11 07:30:00+00:00 44.040001
2024-01-11 08:00:00+00:00 43.940000
2024-01-11 08:30:00+00:00 43.680000
2024-01-11 09:00:00+00:00 43.560000
2024-01-11 09:30:00+00:00 43.599998
2024-01-11 10:00:00+00:00 43.740000
2024-01-11 10:30:00+00:00 43.700001
2024-01-11 11:00:00+00:00 43.940000
2024-01-11 11:30:00+00:00 43.820000
2024-01-11 12:00:00+00:00 43.580000
2024-01-11 12:30:00+00:00 43.980000
2024-01-11 13:00:00+00:00 43.600000
2024-01-11 13:30:00+00:00 43.599998
2024-01-11 14:00:00+00:00 43.620000
2024-01-11 14:30:00+00:00 43.540001
2024-01-11 15:00:00+00:00 43.540000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 2.000000e-02
2018-01-02 11:00:00+03:00 -3.000000e-02
2018-01-02 12:00:00+03:00 -2.000000e-02
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 2.000000e-02
...
2024-01-11 13:00:00+00:00 -3.799995e-01
2024-01-11 13:30:00+00:00 -1.525879e-06
2024-01-11 14:00:00+00:00 2.000153e-02
2024-01-11 14:30:00+00:00 -7.999908e-02
2024-01-11 15:00:00+00:00 -9.155273e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -19.704026393376765
p-value: 0.0
Critical Values: {'1%': -3.4306910426831823, '5%': -2.86169072852345, '10%': -2.5668502287363797}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 3, 1) Log Likelihood -2897.723
Date: Thu, 11 Jan 2024 AIC 5859.446
Time: 21:51:54 BIC 6111.084
Sample: 0 HQIC 5941.933
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.9624 0.003 -299.034 0.000 -0.969 -0.956
ar.L2 -0.9441 0.004 -212.302 0.000 -0.953 -0.935
ar.L3 -0.9047 0.006 -144.716 0.000 -0.917 -0.892
ar.L4 -0.8846 0.007 -122.883 0.000 -0.899 -0.870
ar.L5 -0.8490 0.008 -99.921 0.000 -0.866 -0.832
ar.L6 -0.8395 0.010 -86.966 0.000 -0.858 -0.821
ar.L7 -0.8068 0.010 -79.114 0.000 -0.827 -0.787
ar.L8 -0.7692 0.011 -72.573 0.000 -0.790 -0.748
ar.L9 -0.7254 0.011 -65.625 0.000 -0.747 -0.704
ar.L10 -0.6951 0.012 -58.019 0.000 -0.719 -0.672
ar.L11 -0.6487 0.012 -52.476 0.000 -0.673 -0.624
ar.L12 -0.6180 0.013 -48.410 0.000 -0.643 -0.593
ar.L13 -0.5918 0.013 -45.625 0.000 -0.617 -0.566
ar.L14 -0.5293 0.013 -40.251 0.000 -0.555 -0.503
ar.L15 -0.4989 0.013 -38.310 0.000 -0.524 -0.473
ar.L16 -0.4705 0.013 -36.351 0.000 -0.496 -0.445
ar.L17 -0.4228 0.013 -33.319 0.000 -0.448 -0.398
ar.L18 -0.3793 0.013 -30.215 0.000 -0.404 -0.355
ar.L19 -0.3552 0.012 -29.100 0.000 -0.379 -0.331
ar.L20 -0.3567 0.012 -30.254 0.000 -0.380 -0.334
ar.L21 -0.3614 0.012 -30.807 0.000 -0.384 -0.338
ar.L22 -0.3451 0.011 -30.643 0.000 -0.367 -0.323
ar.L23 -0.3020 0.011 -27.081 0.000 -0.324 -0.280
ar.L24 -0.2660 0.011 -24.733 0.000 -0.287 -0.245
ar.L25 -0.2289 0.010 -22.895 0.000 -0.248 -0.209
ar.L26 -0.1942 0.009 -21.381 0.000 -0.212 -0.176
ar.L27 -0.1609 0.008 -20.215 0.000 -0.176 -0.145
ar.L28 -0.1163 0.007 -16.401 0.000 -0.130 -0.102
ar.L29 -0.0781 0.006 -13.198 0.000 -0.090 -0.067
ar.L30 -0.0551 0.004 -13.570 0.000 -0.063 -0.047
ma.L1 -0.9999 0.002 -578.629 0.000 -1.003 -0.996
sigma2 0.0791 0.000 317.362 0.000 0.079 0.080
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 531593.50
Prob(Q): 0.98 Prob(JB): 0.00
Heteroskedasticity (H): 27.21 Skew: 1.03
Prob(H) (two-sided): 0.00 Kurtosis: 28.68
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 43.584097
19224 43.630074
19225 43.666720
19226 43.676400
19227 43.685185
19228 43.743583
19229 43.778522
19230 43.855908
19231 43.935340
19232 43.966174
Name: predicted_mean, dtype: float64
lower price upper price
19223 43.032733 44.135461
19224 42.835469 44.424679
19225 42.681110 44.652329
19226 42.519536 44.833264
19227 42.373233 44.997137
19228 42.283745 45.203421
19229 42.181162 45.375883
19230 42.124200 45.587617
19231 42.070036 45.800643
19232 41.966258 45.966090
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 89.90 PGSUS
2021-12-27 10:00:00+03:00 91.65 PGSUS
2021-12-27 11:00:00+03:00 90.35 PGSUS
2021-12-27 12:00:00+03:00 90.15 PGSUS
2021-12-27 13:00:00+03:00 90.30 PGSUS
... ... ...
2024-01-11 14:00:00+03:00 712.00 PGSUS
2024-01-11 15:00:00+03:00 708.00 PGSUS
2024-01-11 16:00:00+03:00 710.00 PGSUS
2024-01-11 17:00:00+03:00 712.00 PGSUS
2024-01-11 18:00:00+03:00 711.50 PGSUS
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 89.90
2021-12-27 07:00:00+00:00 91.65
2021-12-27 08:00:00+00:00 90.35
2021-12-27 09:00:00+00:00 90.15
2021-12-27 10:00:00+00:00 90.30
2021-12-27 11:00:00+00:00 88.95
2021-12-27 12:00:00+00:00 89.05
2021-12-27 13:00:00+00:00 88.90
2021-12-27 14:00:00+00:00 88.10
2021-12-27 15:00:00+00:00 87.90
2021-12-28 06:00:00+00:00 89.00
2021-12-28 07:00:00+00:00 88.70
2021-12-28 08:00:00+00:00 88.05
2021-12-28 09:00:00+00:00 88.05
2021-12-28 10:00:00+00:00 87.85
2021-12-28 11:00:00+00:00 87.30
2021-12-28 12:00:00+00:00 87.35
2021-12-28 13:00:00+00:00 86.05
2021-12-28 14:00:00+00:00 86.60
2021-12-28 15:00:00+00:00 86.30
2021-12-29 06:00:00+00:00 85.75
2021-12-29 07:00:00+00:00 84.50
2021-12-29 08:00:00+00:00 87.25
2021-12-29 09:00:00+00:00 87.25
2021-12-29 10:00:00+00:00 87.00
2021-12-29 11:00:00+00:00 87.35
2021-12-29 12:00:00+00:00 87.75
2021-12-29 13:00:00+00:00 89.15
2021-12-29 14:00:00+00:00 88.50
2021-12-29 15:00:00+00:00 88.20
2021-12-30 06:00:00+00:00 88.85
2021-12-30 07:00:00+00:00 87.85
2021-12-30 08:00:00+00:00 87.55
2021-12-30 09:00:00+00:00 86.95
2021-12-30 10:00:00+00:00 86.45
2021-12-30 11:00:00+00:00 85.00
2021-12-30 12:00:00+00:00 84.90
2021-12-30 13:00:00+00:00 85.75
2021-12-30 14:00:00+00:00 85.30
2021-12-30 15:00:00+00:00 85.15
2021-12-31 06:00:00+00:00 86.00
2021-12-31 07:00:00+00:00 87.10
2021-12-31 08:00:00+00:00 86.65
2021-12-31 09:00:00+00:00 87.70
2021-12-31 10:00:00+00:00 86.75
2021-12-31 11:00:00+00:00 86.90
2021-12-31 12:00:00+00:00 85.85
2021-12-31 13:00:00+00:00 85.65
2021-12-31 14:00:00+00:00 84.70
2021-12-31 15:00:00+00:00 84.95
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 702.0
2024-01-10 10:00:00+00:00 706.5
2024-01-10 10:30:00+00:00 704.5
2024-01-10 11:00:00+00:00 705.5
2024-01-10 11:30:00+00:00 708.0
2024-01-10 12:00:00+00:00 704.5
2024-01-10 12:30:00+00:00 711.5
2024-01-10 13:00:00+00:00 711.5
2024-01-10 13:30:00+00:00 712.0
2024-01-10 14:00:00+00:00 711.5
2024-01-10 15:00:00+00:00 711.0
2024-01-11 06:00:00+00:00 720.5
2024-01-11 06:30:00+00:00 718.0
2024-01-11 07:00:00+00:00 712.0
2024-01-11 07:30:00+00:00 713.5
2024-01-11 08:00:00+00:00 712.0
2024-01-11 08:30:00+00:00 713.5
2024-01-11 09:00:00+00:00 713.5
2024-01-11 09:30:00+00:00 713.0
2024-01-11 10:00:00+00:00 712.0
2024-01-11 10:30:00+00:00 711.5
2024-01-11 11:00:00+00:00 712.0
2024-01-11 11:30:00+00:00 711.5
2024-01-11 12:00:00+00:00 708.0
2024-01-11 12:30:00+00:00 713.5
2024-01-11 13:00:00+00:00 710.0
2024-01-11 13:30:00+00:00 710.0
2024-01-11 14:00:00+00:00 712.0
2024-01-11 14:30:00+00:00 711.5
2024-01-11 15:00:00+00:00 711.5
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.30
2018-01-02 11:00:00+03:00 0.74
2018-01-02 12:00:00+03:00 0.08
2018-01-02 13:00:00+03:00 -0.02
2018-01-02 14:00:00+03:00 0.20
...
2024-01-11 13:00:00+00:00 -3.50
2024-01-11 13:30:00+00:00 0.00
2024-01-11 14:00:00+00:00 2.00
2024-01-11 14:30:00+00:00 -0.50
2024-01-11 15:00:00+00:00 0.00
Name: price, Length: 19218, dtype: float64
ADF Statistic: -21.3666017673937
p-value: 0.0
Critical Values: {'1%': -3.430691131638002, '5%': -2.861690767836002, '10%': -2.5668502496617855}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19219
Model: ARIMA(30, 3, 1) Log Likelihood -44135.418
Date: Thu, 11 Jan 2024 AIC 88334.837
Time: 21:56:09 BIC 88586.469
Sample: 0 HQIC 88417.322
- 19219
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.9753 0.002 -400.933 0.000 -0.980 -0.971
ar.L2 -0.9703 0.005 -212.180 0.000 -0.979 -0.961
ar.L3 -0.9504 0.006 -147.763 0.000 -0.963 -0.938
ar.L4 -0.9134 0.008 -119.701 0.000 -0.928 -0.898
ar.L5 -0.8768 0.009 -102.553 0.000 -0.894 -0.860
ar.L6 -0.8203 0.009 -91.343 0.000 -0.838 -0.803
ar.L7 -0.7919 0.010 -81.610 0.000 -0.811 -0.773
ar.L8 -0.7539 0.010 -72.484 0.000 -0.774 -0.734
ar.L9 -0.7200 0.011 -65.966 0.000 -0.741 -0.699
ar.L10 -0.6661 0.011 -58.713 0.000 -0.688 -0.644
ar.L11 -0.6271 0.012 -53.344 0.000 -0.650 -0.604
ar.L12 -0.6030 0.012 -49.885 0.000 -0.627 -0.579
ar.L13 -0.5520 0.012 -44.873 0.000 -0.576 -0.528
ar.L14 -0.5078 0.013 -40.310 0.000 -0.532 -0.483
ar.L15 -0.4942 0.013 -39.334 0.000 -0.519 -0.470
ar.L16 -0.4545 0.013 -36.206 0.000 -0.479 -0.430
ar.L17 -0.4059 0.013 -31.129 0.000 -0.432 -0.380
ar.L18 -0.3546 0.013 -27.797 0.000 -0.380 -0.330
ar.L19 -0.3111 0.013 -24.833 0.000 -0.336 -0.287
ar.L20 -0.3206 0.012 -26.649 0.000 -0.344 -0.297
ar.L21 -0.3276 0.012 -28.465 0.000 -0.350 -0.305
ar.L22 -0.3251 0.011 -29.751 0.000 -0.346 -0.304
ar.L23 -0.2822 0.010 -27.283 0.000 -0.303 -0.262
ar.L24 -0.2632 0.010 -26.064 0.000 -0.283 -0.243
ar.L25 -0.1944 0.010 -20.160 0.000 -0.213 -0.175
ar.L26 -0.1443 0.009 -16.528 0.000 -0.161 -0.127
ar.L27 -0.1088 0.008 -13.726 0.000 -0.124 -0.093
ar.L28 -0.0835 0.007 -11.923 0.000 -0.097 -0.070
ar.L29 -0.0657 0.005 -12.331 0.000 -0.076 -0.055
ar.L30 -0.0360 0.004 -10.109 0.000 -0.043 -0.029
ma.L1 -1.0000 0.007 -150.934 0.000 -1.013 -0.987
sigma2 5.7841 0.035 165.636 0.000 5.716 5.853
===================================================================================
Ljung-Box (L1) (Q): 0.02 Jarque-Bera (JB): 3329222.85
Prob(Q): 0.89 Prob(JB): 0.00
Heteroskedasticity (H): 63.73 Skew: 2.28
Prob(H) (two-sided): 0.00 Kurtosis: 67.32
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19219 711.687011
19220 711.536525
19221 711.644075
19222 711.850896
19223 712.533014
19224 712.680611
19225 712.859428
19226 713.403780
19227 713.231258
19228 713.066544
Name: predicted_mean, dtype: float64
lower price upper price
19219 706.973192 716.400829
19220 704.787079 718.285971
19221 703.328440 719.959711
19222 702.172147 721.529645
19223 701.577801 723.488228
19224 700.504333 724.856889
19225 699.459680 726.259176
19226 698.822363 727.985197
19227 697.481230 728.981286
19228 696.158721 729.974367
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 8.15 PETKM
2021-12-27 10:00:00+03:00 8.39 PETKM
2021-12-27 11:00:00+03:00 8.25 PETKM
2021-12-27 12:00:00+03:00 8.19 PETKM
2021-12-27 13:00:00+03:00 8.18 PETKM
... ... ...
2024-01-11 14:00:00+03:00 20.24 PETKM
2024-01-11 15:00:00+03:00 20.06 PETKM
2024-01-11 16:00:00+03:00 20.10 PETKM
2024-01-11 17:00:00+03:00 20.02 PETKM
2024-01-11 18:00:00+03:00 20.04 PETKM
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 8.15
2021-12-27 07:00:00+00:00 8.39
2021-12-27 08:00:00+00:00 8.25
2021-12-27 09:00:00+00:00 8.19
2021-12-27 10:00:00+00:00 8.18
2021-12-27 11:00:00+00:00 8.12
2021-12-27 12:00:00+00:00 8.06
2021-12-27 13:00:00+00:00 8.06
2021-12-27 14:00:00+00:00 8.01
2021-12-27 15:00:00+00:00 8.01
2021-12-28 06:00:00+00:00 8.12
2021-12-28 07:00:00+00:00 8.06
2021-12-28 08:00:00+00:00 7.97
2021-12-28 09:00:00+00:00 7.93
2021-12-28 10:00:00+00:00 7.95
2021-12-28 11:00:00+00:00 7.88
2021-12-28 12:00:00+00:00 7.92
2021-12-28 13:00:00+00:00 7.90
2021-12-28 14:00:00+00:00 7.84
2021-12-28 15:00:00+00:00 7.82
2021-12-29 06:00:00+00:00 7.73
2021-12-29 07:00:00+00:00 7.77
2021-12-29 08:00:00+00:00 7.86
2021-12-29 09:00:00+00:00 7.88
2021-12-29 10:00:00+00:00 7.94
2021-12-29 11:00:00+00:00 7.95
2021-12-29 12:00:00+00:00 7.99
2021-12-29 13:00:00+00:00 8.03
2021-12-29 14:00:00+00:00 8.03
2021-12-29 15:00:00+00:00 8.05
2021-12-30 06:00:00+00:00 8.15
2021-12-30 07:00:00+00:00 8.10
2021-12-30 08:00:00+00:00 8.11
2021-12-30 09:00:00+00:00 8.03
2021-12-30 10:00:00+00:00 8.02
2021-12-30 11:00:00+00:00 7.89
2021-12-30 12:00:00+00:00 7.86
2021-12-30 13:00:00+00:00 7.90
2021-12-30 14:00:00+00:00 7.78
2021-12-30 15:00:00+00:00 7.74
2021-12-31 06:00:00+00:00 7.74
2021-12-31 07:00:00+00:00 7.82
2021-12-31 08:00:00+00:00 7.90
2021-12-31 09:00:00+00:00 7.91
2021-12-31 10:00:00+00:00 7.84
2021-12-31 11:00:00+00:00 7.86
2021-12-31 12:00:00+00:00 7.80
2021-12-31 13:00:00+00:00 7.81
2021-12-31 14:00:00+00:00 7.78
2021-12-31 15:00:00+00:00 7.80
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 19.930000
2024-01-10 10:00:00+00:00 19.920000
2024-01-10 10:30:00+00:00 19.900000
2024-01-10 11:00:00+00:00 19.880000
2024-01-10 11:30:00+00:00 19.809999
2024-01-10 12:00:00+00:00 19.730000
2024-01-10 12:30:00+00:00 19.860001
2024-01-10 13:00:00+00:00 20.000000
2024-01-10 13:30:00+00:00 20.080000
2024-01-10 14:00:00+00:00 20.220000
2024-01-10 15:00:00+00:00 20.100000
2024-01-11 06:00:00+00:00 20.240000
2024-01-11 06:30:00+00:00 20.260000
2024-01-11 07:00:00+00:00 20.460000
2024-01-11 07:30:00+00:00 20.379999
2024-01-11 08:00:00+00:00 20.300000
2024-01-11 08:30:00+00:00 20.379999
2024-01-11 09:00:00+00:00 20.380000
2024-01-11 09:30:00+00:00 20.280001
2024-01-11 10:00:00+00:00 20.320000
2024-01-11 10:30:00+00:00 20.299999
2024-01-11 11:00:00+00:00 20.240000
2024-01-11 11:30:00+00:00 20.139999
2024-01-11 12:00:00+00:00 20.060000
2024-01-11 12:30:00+00:00 20.299999
2024-01-11 13:00:00+00:00 20.100000
2024-01-11 13:30:00+00:00 20.059999
2024-01-11 14:00:00+00:00 20.020000
2024-01-11 14:30:00+00:00 20.040001
2024-01-11 15:00:00+00:00 20.040000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 3.380000e-02
2018-01-02 11:00:00+03:00 1.120000e-02
2018-01-02 12:00:00+03:00 5.600000e-03
2018-01-02 13:00:00+03:00 5.600000e-03
2018-01-02 14:00:00+03:00 2.820000e-02
...
2024-01-11 13:00:00+00:00 -1.999992e-01
2024-01-11 13:30:00+00:00 -4.000053e-02
2024-01-11 14:00:00+00:00 -3.999947e-02
2024-01-11 14:30:00+00:00 2.000092e-02
2024-01-11 15:00:00+00:00 -9.155273e-07
Name: price, Length: 19223, dtype: float64
ADF Statistic: -21.65032493818614
p-value: 0.0
Critical Values: {'1%': -3.4306909537747425, '5%': -2.8616906892313936, '10%': -2.5668502078218833}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19224
Model: ARIMA(30, 3, 1) Log Likelihood 21432.767
Date: Thu, 11 Jan 2024 AIC -42801.534
Time: 21:59:13 BIC -42549.894
Sample: 0 HQIC -42719.046
- 19224
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.0255 0.007 -154.658 0.000 -1.039 -1.013
ar.L2 -1.0497 0.012 -89.292 0.000 -1.073 -1.027
ar.L3 -1.0584 0.016 -64.587 0.000 -1.091 -1.026
ar.L4 -1.0711 0.020 -53.438 0.000 -1.110 -1.032
ar.L5 -1.0603 0.023 -45.387 0.000 -1.106 -1.014
ar.L6 -1.0418 0.026 -40.243 0.000 -1.093 -0.991
ar.L7 -1.0339 0.028 -37.034 0.000 -1.089 -0.979
ar.L8 -1.0208 0.029 -34.725 0.000 -1.078 -0.963
ar.L9 -1.0043 0.031 -32.586 0.000 -1.065 -0.944
ar.L10 -0.9765 0.032 -30.938 0.000 -1.038 -0.915
ar.L11 -0.9243 0.032 -28.751 0.000 -0.987 -0.861
ar.L12 -0.8980 0.032 -27.851 0.000 -0.961 -0.835
ar.L13 -0.8541 0.032 -26.483 0.000 -0.917 -0.791
ar.L14 -0.8254 0.032 -26.124 0.000 -0.887 -0.763
ar.L15 -0.7934 0.031 -25.525 0.000 -0.854 -0.732
ar.L16 -0.7439 0.030 -24.466 0.000 -0.804 -0.684
ar.L17 -0.6839 0.029 -23.337 0.000 -0.741 -0.626
ar.L18 -0.6172 0.028 -21.839 0.000 -0.673 -0.562
ar.L19 -0.5756 0.027 -21.391 0.000 -0.628 -0.523
ar.L20 -0.5494 0.025 -21.720 0.000 -0.599 -0.500
ar.L21 -0.5461 0.023 -23.348 0.000 -0.592 -0.500
ar.L22 -0.5150 0.022 -23.340 0.000 -0.558 -0.472
ar.L23 -0.4646 0.020 -22.795 0.000 -0.505 -0.425
ar.L24 -0.4160 0.019 -22.154 0.000 -0.453 -0.379
ar.L25 -0.3397 0.017 -20.356 0.000 -0.372 -0.307
ar.L26 -0.2837 0.014 -19.591 0.000 -0.312 -0.255
ar.L27 -0.2333 0.012 -18.846 0.000 -0.258 -0.209
ar.L28 -0.1834 0.010 -18.386 0.000 -0.203 -0.164
ar.L29 -0.1120 0.008 -14.715 0.000 -0.127 -0.097
ar.L30 -0.0497 0.005 -10.114 0.000 -0.059 -0.040
ma.L1 -0.8683 0.006 -142.675 0.000 -0.880 -0.856
sigma2 0.0062 2.01e-05 310.017 0.000 0.006 0.006
===================================================================================
Ljung-Box (L1) (Q): 0.75 Jarque-Bera (JB): 411600.59
Prob(Q): 0.39 Prob(JB): 0.00
Heteroskedasticity (H): 25.29 Skew: 0.67
Prob(H) (two-sided): 0.00 Kurtosis: 25.63
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224 20.016772
19225 19.994482
19226 19.973368
19227 19.947934
19228 19.927790
19229 19.916609
19230 19.906461
19231 19.901454
19232 19.886047
19233 19.857638
Name: predicted_mean, dtype: float64
lower price upper price
19224 19.861893 20.171650
19225 19.763531 20.225433
19226 19.675854 20.270882
19227 19.586182 20.309685
19228 19.502715 20.352866
19229 19.426449 20.406770
19230 19.348687 20.464235
19231 19.274251 20.528656
19232 19.187115 20.584980
19233 19.084403 20.630873
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 12.4338 SAHOL
2021-12-27 10:00:00+03:00 12.5345 SAHOL
2021-12-27 11:00:00+03:00 12.4978 SAHOL
2021-12-27 12:00:00+03:00 12.4978 SAHOL
2021-12-27 13:00:00+03:00 12.5985 SAHOL
... ... ...
2024-01-11 14:00:00+03:00 66.3000 SAHOL
2024-01-11 15:00:00+03:00 67.1500 SAHOL
2024-01-11 16:00:00+03:00 68.0000 SAHOL
2024-01-11 17:00:00+03:00 68.4500 SAHOL
2024-01-11 18:00:00+03:00 68.3500 SAHOL
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 12.4338
2021-12-27 07:00:00+00:00 12.5345
2021-12-27 08:00:00+00:00 12.4978
2021-12-27 09:00:00+00:00 12.4978
2021-12-27 10:00:00+00:00 12.5985
2021-12-27 11:00:00+00:00 12.4155
2021-12-27 12:00:00+00:00 12.3606
2021-12-27 13:00:00+00:00 12.3240
2021-12-27 14:00:00+00:00 12.2416
2021-12-27 15:00:00+00:00 12.2325
2021-12-28 06:00:00+00:00 12.3514
2021-12-28 07:00:00+00:00 12.3148
2021-12-28 08:00:00+00:00 12.2050
2021-12-28 09:00:00+00:00 12.1410
2021-12-28 10:00:00+00:00 12.0587
2021-12-28 11:00:00+00:00 11.9489
2021-12-28 12:00:00+00:00 12.0495
2021-12-28 13:00:00+00:00 11.9763
2021-12-28 14:00:00+00:00 11.9214
2021-12-28 15:00:00+00:00 11.9123
2021-12-29 06:00:00+00:00 11.8574
2021-12-29 07:00:00+00:00 11.7934
2021-12-29 08:00:00+00:00 11.9672
2021-12-29 09:00:00+00:00 12.0038
2021-12-29 10:00:00+00:00 12.0403
2021-12-29 11:00:00+00:00 12.0221
2021-12-29 12:00:00+00:00 12.1135
2021-12-29 13:00:00+00:00 12.2325
2021-12-29 14:00:00+00:00 12.1685
2021-12-29 15:00:00+00:00 12.2050
2021-12-30 06:00:00+00:00 12.2782
2021-12-30 07:00:00+00:00 12.1410
2021-12-30 08:00:00+00:00 12.2416
2021-12-30 09:00:00+00:00 12.1685
2021-12-30 10:00:00+00:00 12.2142
2021-12-30 11:00:00+00:00 12.0221
2021-12-30 12:00:00+00:00 12.0861
2021-12-30 13:00:00+00:00 12.1501
2021-12-30 14:00:00+00:00 12.0221
2021-12-30 15:00:00+00:00 11.9672
2021-12-31 06:00:00+00:00 11.9855
2021-12-31 07:00:00+00:00 12.0221
2021-12-31 08:00:00+00:00 12.0953
2021-12-31 09:00:00+00:00 12.1319
2021-12-31 10:00:00+00:00 12.0495
2021-12-31 11:00:00+00:00 12.0769
2021-12-31 12:00:00+00:00 12.0587
2021-12-31 13:00:00+00:00 12.0953
2021-12-31 14:00:00+00:00 12.0587
2021-12-31 15:00:00+00:00 12.1045
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 63.500000
2024-01-10 10:00:00+00:00 63.600000
2024-01-10 10:30:00+00:00 63.900002
2024-01-10 11:00:00+00:00 64.400000
2024-01-10 11:30:00+00:00 64.400002
2024-01-10 12:00:00+00:00 64.200000
2024-01-10 12:30:00+00:00 65.199997
2024-01-10 13:00:00+00:00 65.200000
2024-01-10 13:30:00+00:00 65.449997
2024-01-10 14:00:00+00:00 66.000000
2024-01-10 15:00:00+00:00 66.000000
2024-01-11 06:00:00+00:00 66.000000
2024-01-11 06:30:00+00:00 66.250000
2024-01-11 07:00:00+00:00 65.750000
2024-01-11 07:30:00+00:00 65.849998
2024-01-11 08:00:00+00:00 65.800000
2024-01-11 08:30:00+00:00 66.199997
2024-01-11 09:00:00+00:00 65.950000
2024-01-11 09:30:00+00:00 65.750000
2024-01-11 10:00:00+00:00 65.600000
2024-01-11 10:30:00+00:00 65.949997
2024-01-11 11:00:00+00:00 66.300000
2024-01-11 11:30:00+00:00 67.250000
2024-01-11 12:00:00+00:00 67.150000
2024-01-11 12:30:00+00:00 68.550003
2024-01-11 13:00:00+00:00 68.000000
2024-01-11 13:30:00+00:00 67.900002
2024-01-11 14:00:00+00:00 68.450000
2024-01-11 14:30:00+00:00 68.349998
2024-01-11 15:00:00+00:00 68.350000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.000000
2018-01-02 11:00:00+03:00 0.063000
2018-01-02 12:00:00+03:00 -0.031500
2018-01-02 13:00:00+03:00 0.023600
2018-01-02 14:00:00+03:00 0.015700
...
2024-01-11 13:00:00+00:00 -0.550003
2024-01-11 13:30:00+00:00 -0.099998
2024-01-11 14:00:00+00:00 0.549998
2024-01-11 14:30:00+00:00 -0.100002
2024-01-11 15:00:00+00:00 0.000002
Name: price, Length: 19222, dtype: float64
ADF Statistic: -22.133374861915275
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 1, 1) Log Likelihood -2883.804
Date: Thu, 11 Jan 2024 AIC 5831.607
Time: 22:02:01 BIC 6083.249
Sample: 0 HQIC 5914.095
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.3601 0.010 -142.008 0.000 -1.379 -1.341
ar.L2 -0.4891 0.008 -61.543 0.000 -0.505 -0.474
ar.L3 -0.1094 0.007 -15.244 0.000 -0.123 -0.095
ar.L4 -0.0305 0.008 -3.908 0.000 -0.046 -0.015
ar.L5 0.0195 0.008 2.459 0.014 0.004 0.035
ar.L6 0.0259 0.008 3.255 0.001 0.010 0.041
ar.L7 0.0304 0.008 3.771 0.000 0.015 0.046
ar.L8 -0.0078 0.008 -0.916 0.360 -0.024 0.009
ar.L9 -0.0505 0.009 -5.763 0.000 -0.068 -0.033
ar.L10 0.0153 0.009 1.709 0.087 -0.002 0.033
ar.L11 0.0333 0.009 3.900 0.000 0.017 0.050
ar.L12 0.0058 0.009 0.681 0.496 -0.011 0.023
ar.L13 0.0262 0.009 3.064 0.002 0.009 0.043
ar.L14 -0.0149 0.008 -1.775 0.076 -0.031 0.002
ar.L15 -0.0066 0.008 -0.811 0.417 -0.023 0.009
ar.L16 -0.1115 0.008 -14.747 0.000 -0.126 -0.097
ar.L17 -0.2205 0.007 -33.132 0.000 -0.234 -0.207
ar.L18 0.2221 0.006 34.853 0.000 0.210 0.235
ar.L19 0.5016 0.006 79.275 0.000 0.489 0.514
ar.L20 -0.0156 0.006 -2.401 0.016 -0.028 -0.003
ar.L21 -0.2478 0.007 -35.970 0.000 -0.261 -0.234
ar.L22 -0.1125 0.008 -14.177 0.000 -0.128 -0.097
ar.L23 -0.0852 0.008 -10.770 0.000 -0.101 -0.070
ar.L24 -0.0416 0.008 -5.160 0.000 -0.057 -0.026
ar.L25 -0.0179 0.008 -2.141 0.032 -0.034 -0.002
ar.L26 0.0404 0.009 4.644 0.000 0.023 0.057
ar.L27 0.0524 0.009 5.652 0.000 0.034 0.071
ar.L28 -0.0101 0.009 -1.080 0.280 -0.029 0.008
ar.L29 0.0069 0.009 0.794 0.427 -0.010 0.024
ar.L30 0.0873 0.006 14.595 0.000 0.076 0.099
ma.L1 0.7443 0.009 79.886 0.000 0.726 0.763
sigma2 0.0807 0.000 227.877 0.000 0.080 0.081
===================================================================================
Ljung-Box (L1) (Q): 12.08 Jarque-Bera (JB): 146099.09
Prob(Q): 0.00 Prob(JB): 0.00
Heteroskedasticity (H): 52.53 Skew: 0.02
Prob(H) (two-sided): 0.00 Kurtosis: 16.51
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 68.384584
19224 68.217454
19225 68.181553
19226 68.291383
19227 68.472170
19228 68.476582
19229 68.276035
19230 68.222928
19231 68.248807
19232 68.404555
Name: predicted_mean, dtype: float64
lower price upper price
19223 67.827951 68.941218
19224 67.621156 68.813752
19225 67.459149 68.903956
19226 67.526687 69.056078
19227 67.613780 69.330560
19228 67.574424 69.378740
19229 67.296602 69.255467
19230 67.199561 69.246294
19231 67.165187 69.332427
19232 67.283371 69.525738
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 10.5956 SASA
2021-12-27 10:00:00+03:00 11.0217 SASA
2021-12-27 11:00:00+03:00 11.0108 SASA
2021-12-27 12:00:00+03:00 11.0326 SASA
2021-12-27 13:00:00+03:00 11.0108 SASA
... ... ...
2024-01-11 14:00:00+03:00 35.4600 SASA
2024-01-11 15:00:00+03:00 35.1400 SASA
2024-01-11 16:00:00+03:00 35.2000 SASA
2024-01-11 17:00:00+03:00 35.2400 SASA
2024-01-11 18:00:00+03:00 35.0800 SASA
[5102 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 10.5956
2021-12-27 07:00:00+00:00 11.0217
2021-12-27 08:00:00+00:00 11.0108
2021-12-27 09:00:00+00:00 11.0326
2021-12-27 10:00:00+00:00 11.0108
2021-12-27 11:00:00+00:00 10.9348
2021-12-27 12:00:00+00:00 10.8696
2021-12-27 13:00:00+00:00 11.0000
2021-12-27 14:00:00+00:00 11.1196
2021-12-27 15:00:00+00:00 11.1630
2021-12-28 06:00:00+00:00 11.3913
2021-12-28 07:00:00+00:00 11.4239
2021-12-28 08:00:00+00:00 11.2608
2021-12-28 09:00:00+00:00 11.2283
2021-12-28 10:00:00+00:00 11.1957
2021-12-28 11:00:00+00:00 11.1739
2021-12-28 12:00:00+00:00 11.1848
2021-12-28 13:00:00+00:00 11.2173
2021-12-28 14:00:00+00:00 11.4022
2021-12-28 15:00:00+00:00 11.4783
2021-12-29 06:00:00+00:00 11.5761
2021-12-29 07:00:00+00:00 11.4457
2021-12-29 08:00:00+00:00 11.5000
2021-12-29 09:00:00+00:00 11.5000
2021-12-29 10:00:00+00:00 11.4891
2021-12-29 11:00:00+00:00 11.5435
2021-12-29 12:00:00+00:00 11.4783
2021-12-29 13:00:00+00:00 11.4891
2021-12-29 14:00:00+00:00 11.6413
2021-12-29 15:00:00+00:00 11.5979
2021-12-30 06:00:00+00:00 11.7391
2021-12-30 07:00:00+00:00 11.7609
2021-12-30 08:00:00+00:00 11.7935
2021-12-30 09:00:00+00:00 11.7609
2021-12-30 10:00:00+00:00 11.8478
2021-12-30 11:00:00+00:00 11.9891
2021-12-30 12:00:00+00:00 11.9239
2021-12-30 13:00:00+00:00 12.0217
2021-12-30 14:00:00+00:00 11.8913
2021-12-30 15:00:00+00:00 11.8370
2021-12-31 06:00:00+00:00 11.7826
2021-12-31 07:00:00+00:00 11.6630
2021-12-31 08:00:00+00:00 11.7173
2021-12-31 09:00:00+00:00 11.7283
2021-12-31 10:00:00+00:00 11.6957
2021-12-31 11:00:00+00:00 11.6630
2021-12-31 12:00:00+00:00 11.6522
2021-12-31 13:00:00+00:00 11.6413
2021-12-31 14:00:00+00:00 11.5108
2021-12-31 15:00:00+00:00 11.4891
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 35.680000
2024-01-10 10:00:00+00:00 35.560000
2024-01-10 10:30:00+00:00 35.560001
2024-01-10 11:00:00+00:00 35.580000
2024-01-10 11:30:00+00:00 35.540001
2024-01-10 12:00:00+00:00 35.320000
2024-01-10 12:30:00+00:00 35.599998
2024-01-10 13:00:00+00:00 35.760000
2024-01-10 13:30:00+00:00 35.680000
2024-01-10 14:00:00+00:00 35.700000
2024-01-10 15:00:00+00:00 35.780000
2024-01-11 06:00:00+00:00 35.880000
2024-01-11 06:30:00+00:00 36.000000
2024-01-11 07:00:00+00:00 35.800000
2024-01-11 07:30:00+00:00 35.740002
2024-01-11 08:00:00+00:00 35.500000
2024-01-11 08:30:00+00:00 35.540001
2024-01-11 09:00:00+00:00 35.540000
2024-01-11 09:30:00+00:00 35.500000
2024-01-11 10:00:00+00:00 35.460000
2024-01-11 10:30:00+00:00 35.419998
2024-01-11 11:00:00+00:00 35.460000
2024-01-11 11:30:00+00:00 35.279999
2024-01-11 12:00:00+00:00 35.140000
2024-01-11 12:30:00+00:00 35.380001
2024-01-11 13:00:00+00:00 35.200000
2024-01-11 13:30:00+00:00 35.220001
2024-01-11 14:00:00+00:00 35.240000
2024-01-11 14:30:00+00:00 35.080002
2024-01-11 15:00:00+00:00 35.080000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.006900
2018-01-02 11:00:00+03:00 0.027700
2018-01-02 12:00:00+03:00 0.019300
2018-01-02 13:00:00+03:00 0.000000
2018-01-02 14:00:00+03:00 0.000000
...
2024-01-11 13:00:00+00:00 -0.180001
2024-01-11 13:30:00+00:00 0.020001
2024-01-11 14:00:00+00:00 0.019999
2024-01-11 14:30:00+00:00 -0.159998
2024-01-11 15:00:00+00:00 -0.000002
Name: price, Length: 19220, dtype: float64
ADF Statistic: -22.379868581384628
p-value: 0.0
Critical Values: {'1%': -3.4306910071142425, '5%': -2.8616907128041684, '10%': -2.5668502203692722}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19221
Model: ARIMA(30, 1, 1) Log Likelihood -5300.323
Date: Thu, 11 Jan 2024 AIC 10664.646
Time: 22:04:46 BIC 10916.284
Sample: 0 HQIC 10747.133
- 19221
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.0894 0.004 -267.476 0.000 -1.097 -1.081
ar.L2 -0.1027 0.005 -21.763 0.000 -0.112 -0.093
ar.L3 -0.0254 0.005 -4.620 0.000 -0.036 -0.015
ar.L4 -0.0345 0.006 -6.151 0.000 -0.045 -0.023
ar.L5 -0.0167 0.005 -3.504 0.000 -0.026 -0.007
ar.L6 -0.0201 0.005 -4.198 0.000 -0.029 -0.011
ar.L7 -0.0073 0.005 -1.351 0.177 -0.018 0.003
ar.L8 0.0042 0.007 0.637 0.524 -0.009 0.017
ar.L9 0.0395 0.007 5.649 0.000 0.026 0.053
ar.L10 0.0430 0.007 6.465 0.000 0.030 0.056
ar.L11 0.0287 0.006 4.467 0.000 0.016 0.041
ar.L12 -0.0012 0.006 -0.198 0.843 -0.013 0.010
ar.L13 0.0127 0.005 2.353 0.019 0.002 0.023
ar.L14 0.0002 0.005 0.037 0.971 -0.010 0.011
ar.L15 0.0192 0.006 3.330 0.001 0.008 0.030
ar.L16 0.0481 0.006 8.560 0.000 0.037 0.059
ar.L17 0.0927 0.004 25.654 0.000 0.086 0.100
ar.L18 0.2211 0.002 91.579 0.000 0.216 0.226
ar.L19 0.1657 0.002 68.764 0.000 0.161 0.170
ar.L20 -0.0642 0.004 -17.711 0.000 -0.071 -0.057
ar.L21 -0.0452 0.005 -9.226 0.000 -0.055 -0.036
ar.L22 -0.0325 0.004 -7.328 0.000 -0.041 -0.024
ar.L23 -0.0661 0.005 -12.386 0.000 -0.077 -0.056
ar.L24 -0.0344 0.005 -6.394 0.000 -0.045 -0.024
ar.L25 -0.0532 0.005 -10.354 0.000 -0.063 -0.043
ar.L26 0.0011 0.006 0.172 0.864 -0.011 0.013
ar.L27 -0.0073 0.006 -1.309 0.191 -0.018 0.004
ar.L28 -0.0344 0.006 -5.444 0.000 -0.047 -0.022
ar.L29 -0.0198 0.006 -3.281 0.001 -0.032 -0.008
ar.L30 0.0280 0.004 7.746 0.000 0.021 0.035
ma.L1 0.9125 0.003 295.646 0.000 0.906 0.919
sigma2 0.1016 0.000 490.524 0.000 0.101 0.102
===================================================================================
Ljung-Box (L1) (Q): 0.02 Jarque-Bera (JB): 14561490.06
Prob(Q): 0.88 Prob(JB): 0.00
Heteroskedasticity (H): 1631.44 Skew: -0.86
Prob(H) (two-sided): 0.00 Kurtosis: 137.83
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19221 35.072892
19222 35.007492
19223 34.988249
19224 34.964652
19225 34.954591
19226 34.962518
19227 34.958805
19228 34.957794
19229 34.952837
19230 34.945019
Name: predicted_mean, dtype: float64
lower price upper price
19221 34.448093 35.697690
19222 34.198263 35.816721
19223 33.998117 35.978380
19224 33.853288 36.076016
19225 33.713733 36.195449
19226 33.624987 36.300048
19227 33.516081 36.401529
19228 33.431299 36.484289
19229 33.332503 36.573170
19230 33.242014 36.648023
price short_name
timestamp
2021-12-27 09:00:00+03:00 13.3510 SISE
2021-12-27 10:00:00+03:00 13.5826 SISE
2021-12-27 11:00:00+03:00 13.4379 SISE
2021-12-27 12:00:00+03:00 13.4282 SISE
2021-12-27 13:00:00+03:00 13.4186 SISE
... ... ...
2024-01-11 14:00:00+03:00 48.1400 SISE
2024-01-11 15:00:00+03:00 47.9200 SISE
2024-01-11 16:00:00+03:00 47.8600 SISE
2024-01-11 17:00:00+03:00 47.8600 SISE
2024-01-11 18:00:00+03:00 47.8000 SISE
[5104 rows x 2 columns]
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 13.3510
2021-12-27 07:00:00+00:00 13.5826
2021-12-27 08:00:00+00:00 13.4379
2021-12-27 09:00:00+00:00 13.4282
2021-12-27 10:00:00+00:00 13.4186
2021-12-27 11:00:00+00:00 13.3028
2021-12-27 12:00:00+00:00 13.1870
2021-12-27 13:00:00+00:00 13.2352
2021-12-27 14:00:00+00:00 13.1099
2021-12-27 15:00:00+00:00 13.0712
2021-12-28 06:00:00+00:00 13.3221
2021-12-28 07:00:00+00:00 13.4861
2021-12-28 08:00:00+00:00 13.4282
2021-12-28 09:00:00+00:00 13.3896
2021-12-28 10:00:00+00:00 13.3606
2021-12-28 11:00:00+00:00 13.1870
2021-12-28 12:00:00+00:00 13.2739
2021-12-28 13:00:00+00:00 13.1581
2021-12-28 14:00:00+00:00 12.9941
2021-12-28 15:00:00+00:00 12.9363
2021-12-29 06:00:00+00:00 12.9265
2021-12-29 07:00:00+00:00 12.9169
2021-12-29 08:00:00+00:00 13.1677
2021-12-29 09:00:00+00:00 13.2256
2021-12-29 10:00:00+00:00 13.2063
2021-12-29 11:00:00+00:00 13.2063
2021-12-29 12:00:00+00:00 13.3124
2021-12-29 13:00:00+00:00 13.3703
2021-12-29 14:00:00+00:00 13.3703
2021-12-29 15:00:00+00:00 13.3606
2021-12-30 06:00:00+00:00 13.6019
2021-12-30 07:00:00+00:00 13.3993
2021-12-30 08:00:00+00:00 13.4186
2021-12-30 09:00:00+00:00 13.3124
2021-12-30 10:00:00+00:00 13.3414
2021-12-30 11:00:00+00:00 13.1485
2021-12-30 12:00:00+00:00 13.1099
2021-12-30 13:00:00+00:00 13.1774
2021-12-30 14:00:00+00:00 13.0134
2021-12-30 15:00:00+00:00 12.9941
2021-12-31 06:00:00+00:00 12.9941
2021-12-31 07:00:00+00:00 13.0712
2021-12-31 08:00:00+00:00 13.2642
2021-12-31 09:00:00+00:00 13.3221
2021-12-31 10:00:00+00:00 13.0712
2021-12-31 11:00:00+00:00 13.1388
2021-12-31 12:00:00+00:00 13.0906
2021-12-31 13:00:00+00:00 13.0230
2021-12-31 14:00:00+00:00 12.9265
2021-12-31 15:00:00+00:00 12.9072
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 47.599998
2024-01-10 10:00:00+00:00 47.700000
2024-01-10 10:30:00+00:00 47.720001
2024-01-10 11:00:00+00:00 47.700000
2024-01-10 11:30:00+00:00 47.720001
2024-01-10 12:00:00+00:00 47.480000
2024-01-10 12:30:00+00:00 47.840000
2024-01-10 13:00:00+00:00 48.040000
2024-01-10 13:30:00+00:00 48.180000
2024-01-10 14:00:00+00:00 48.420000
2024-01-10 15:00:00+00:00 48.460000
2024-01-11 06:00:00+00:00 48.460000
2024-01-11 06:30:00+00:00 48.459999
2024-01-11 07:00:00+00:00 48.320000
2024-01-11 07:30:00+00:00 48.220001
2024-01-11 08:00:00+00:00 48.080000
2024-01-11 08:30:00+00:00 48.320000
2024-01-11 09:00:00+00:00 48.340000
2024-01-11 09:30:00+00:00 48.299999
2024-01-11 10:00:00+00:00 48.240000
2024-01-11 10:30:00+00:00 48.200001
2024-01-11 11:00:00+00:00 48.140000
2024-01-11 11:30:00+00:00 48.080002
2024-01-11 12:00:00+00:00 47.920000
2024-01-11 12:30:00+00:00 48.220001
2024-01-11 13:00:00+00:00 47.860000
2024-01-11 13:30:00+00:00 47.820000
2024-01-11 14:00:00+00:00 47.860000
2024-01-11 14:30:00+00:00 47.799999
2024-01-11 15:00:00+00:00 47.800000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 -5.130000e-02
2018-01-02 11:00:00+03:00 2.560000e-02
2018-01-02 12:00:00+03:00 0.000000e+00
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 2.570000e-02
...
2024-01-11 13:00:00+00:00 -3.600012e-01
2024-01-11 13:30:00+00:00 -4.000031e-02
2024-01-11 14:00:00+00:00 4.000031e-02
2024-01-11 14:30:00+00:00 -6.000076e-02
2024-01-11 15:00:00+00:00 7.629395e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -22.219929304142656
p-value: 0.0
Critical Values: {'1%': -3.4306910604704344, '5%': -2.86169073638432, '10%': -2.566850232920588}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 1, 1) Log Likelihood 3630.416
Date: Thu, 11 Jan 2024 AIC -7196.832
Time: 22:07:40 BIC -6945.190
Sample: 0 HQIC -7114.344
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.1790 0.005 -243.250 0.000 -1.189 -1.170
ar.L2 -0.1649 0.004 -37.298 0.000 -0.174 -0.156
ar.L3 0.0057 0.006 1.014 0.311 -0.005 0.017
ar.L4 -0.0372 0.006 -5.955 0.000 -0.049 -0.025
ar.L5 0.0027 0.006 0.459 0.646 -0.009 0.014
ar.L6 -0.0157 0.006 -2.624 0.009 -0.027 -0.004
ar.L7 -0.0218 0.006 -3.648 0.000 -0.034 -0.010
ar.L8 -0.0186 0.006 -2.962 0.003 -0.031 -0.006
ar.L9 0.0083 0.006 1.317 0.188 -0.004 0.021
ar.L10 0.0554 0.007 8.025 0.000 0.042 0.069
ar.L11 0.0579 0.007 8.439 0.000 0.044 0.071
ar.L12 0.0259 0.007 3.711 0.000 0.012 0.040
ar.L13 0.0323 0.007 4.736 0.000 0.019 0.046
ar.L14 0.0178 0.007 2.693 0.007 0.005 0.031
ar.L15 0.0077 0.007 1.158 0.247 -0.005 0.021
ar.L16 -0.0439 0.006 -7.028 0.000 -0.056 -0.032
ar.L17 -0.0441 0.006 -7.670 0.000 -0.055 -0.033
ar.L18 0.0896 0.005 17.477 0.000 0.080 0.100
ar.L19 0.1744 0.005 38.572 0.000 0.166 0.183
ar.L20 -0.0715 0.005 -13.340 0.000 -0.082 -0.061
ar.L21 -0.1753 0.006 -31.174 0.000 -0.186 -0.164
ar.L22 -0.0612 0.007 -8.841 0.000 -0.075 -0.048
ar.L23 -0.0516 0.006 -8.039 0.000 -0.064 -0.039
ar.L24 -0.0112 0.006 -1.800 0.072 -0.023 0.001
ar.L25 0.0145 0.006 2.364 0.018 0.002 0.027
ar.L26 0.0308 0.007 4.638 0.000 0.018 0.044
ar.L27 0.0009 0.007 0.127 0.899 -0.013 0.015
ar.L28 -0.0349 0.008 -4.594 0.000 -0.050 -0.020
ar.L29 -0.0151 0.007 -2.109 0.035 -0.029 -0.001
ar.L30 0.0527 0.005 10.223 0.000 0.043 0.063
ma.L1 0.8728 0.004 220.299 0.000 0.865 0.881
sigma2 0.0401 0.000 289.728 0.000 0.040 0.040
===================================================================================
Ljung-Box (L1) (Q): 0.24 Jarque-Bera (JB): 530239.91
Prob(Q): 0.63 Prob(JB): 0.00
Heteroskedasticity (H): 65.26 Skew: 0.79
Prob(H) (two-sided): 0.00 Kurtosis: 28.68
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 47.752453
19224 47.772268
19225 47.710635
19226 47.751871
19227 47.745092
19228 47.802465
19229 47.756287
19230 47.760609
19231 47.740262
19232 47.756066
Name: predicted_mean, dtype: float64
lower price upper price
19223 47.359777 48.145129
19224 47.294345 48.250192
19225 47.118574 48.302696
19226 47.096645 48.407097
19227 47.009750 48.480435
19228 47.012552 48.592378
19229 46.902748 48.609825
19230 46.860389 48.660828
19231 46.786207 48.694317
19232 46.757779 48.754354
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 33.24 TAVHL
2021-12-27 10:00:00+03:00 33.68 TAVHL
2021-12-27 11:00:00+03:00 33.36 TAVHL
2021-12-27 12:00:00+03:00 33.20 TAVHL
2021-12-27 13:00:00+03:00 33.20 TAVHL
... ... ...
2024-01-11 14:00:00+03:00 117.30 TAVHL
2024-01-11 15:00:00+03:00 116.30 TAVHL
2024-01-11 16:00:00+03:00 117.00 TAVHL
2024-01-11 17:00:00+03:00 117.80 TAVHL
2024-01-11 18:00:00+03:00 117.80 TAVHL
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 33.24
2021-12-27 07:00:00+00:00 33.68
2021-12-27 08:00:00+00:00 33.36
2021-12-27 09:00:00+00:00 33.20
2021-12-27 10:00:00+00:00 33.20
2021-12-27 11:00:00+00:00 32.64
2021-12-27 12:00:00+00:00 32.76
2021-12-27 13:00:00+00:00 32.78
2021-12-27 14:00:00+00:00 32.60
2021-12-27 15:00:00+00:00 32.50
2021-12-28 06:00:00+00:00 32.60
2021-12-28 07:00:00+00:00 32.90
2021-12-28 08:00:00+00:00 32.70
2021-12-28 09:00:00+00:00 32.56
2021-12-28 10:00:00+00:00 32.42
2021-12-28 11:00:00+00:00 32.18
2021-12-28 12:00:00+00:00 32.30
2021-12-28 13:00:00+00:00 31.50
2021-12-28 14:00:00+00:00 31.54
2021-12-28 15:00:00+00:00 31.56
2021-12-29 06:00:00+00:00 31.12
2021-12-29 07:00:00+00:00 31.22
2021-12-29 08:00:00+00:00 31.56
2021-12-29 09:00:00+00:00 31.78
2021-12-29 10:00:00+00:00 31.78
2021-12-29 11:00:00+00:00 32.14
2021-12-29 12:00:00+00:00 32.50
2021-12-29 13:00:00+00:00 32.74
2021-12-29 14:00:00+00:00 32.38
2021-12-29 15:00:00+00:00 32.50
2021-12-30 06:00:00+00:00 32.98
2021-12-30 07:00:00+00:00 32.10
2021-12-30 08:00:00+00:00 32.10
2021-12-30 09:00:00+00:00 31.92
2021-12-30 10:00:00+00:00 31.80
2021-12-30 11:00:00+00:00 31.34
2021-12-30 12:00:00+00:00 31.48
2021-12-30 13:00:00+00:00 31.48
2021-12-30 14:00:00+00:00 31.16
2021-12-30 15:00:00+00:00 31.08
2021-12-31 06:00:00+00:00 31.58
2021-12-31 07:00:00+00:00 32.44
2021-12-31 08:00:00+00:00 32.36
2021-12-31 09:00:00+00:00 33.00
2021-12-31 10:00:00+00:00 32.50
2021-12-31 11:00:00+00:00 32.60
2021-12-31 12:00:00+00:00 32.26
2021-12-31 13:00:00+00:00 32.18
2021-12-31 14:00:00+00:00 31.96
2021-12-31 15:00:00+00:00 32.70
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 117.400002
2024-01-10 10:00:00+00:00 117.700000
2024-01-10 10:30:00+00:00 117.400002
2024-01-10 11:00:00+00:00 117.500000
2024-01-10 11:30:00+00:00 117.300003
2024-01-10 12:00:00+00:00 116.900000
2024-01-10 12:30:00+00:00 117.099998
2024-01-10 13:00:00+00:00 117.800000
2024-01-10 13:30:00+00:00 117.500000
2024-01-10 14:00:00+00:00 117.400000
2024-01-10 15:00:00+00:00 117.000000
2024-01-11 06:00:00+00:00 117.500000
2024-01-11 06:30:00+00:00 118.300003
2024-01-11 07:00:00+00:00 116.800000
2024-01-11 07:30:00+00:00 117.400002
2024-01-11 08:00:00+00:00 117.000000
2024-01-11 08:30:00+00:00 117.199997
2024-01-11 09:00:00+00:00 117.100000
2024-01-11 09:30:00+00:00 117.000000
2024-01-11 10:00:00+00:00 117.000000
2024-01-11 10:30:00+00:00 116.800003
2024-01-11 11:00:00+00:00 117.300000
2024-01-11 11:30:00+00:00 117.000000
2024-01-11 12:00:00+00:00 116.300000
2024-01-11 12:30:00+00:00 117.400002
2024-01-11 13:00:00+00:00 117.000000
2024-01-11 13:30:00+00:00 116.900002
2024-01-11 14:00:00+00:00 117.800000
2024-01-11 14:30:00+00:00 117.800003
2024-01-11 15:00:00+00:00 117.800000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.163500
2018-01-02 11:00:00+03:00 -0.065400
2018-01-02 12:00:00+03:00 0.049000
2018-01-02 13:00:00+03:00 0.000000
2018-01-02 14:00:00+03:00 -0.016400
...
2024-01-11 13:00:00+00:00 -0.400002
2024-01-11 13:30:00+00:00 -0.099998
2024-01-11 14:00:00+00:00 0.899998
2024-01-11 14:30:00+00:00 0.000003
2024-01-11 15:00:00+00:00 -0.000003
Name: price, Length: 19222, dtype: float64
ADF Statistic: -22.076741212277536
p-value: 0.0
Critical Values: {'1%': -3.4306910426831823, '5%': -2.86169072852345, '10%': -2.5668502287363797}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 3, 1) Log Likelihood -10657.133
Date: Thu, 11 Jan 2024 AIC 21378.265
Time: 22:11:40 BIC 21629.904
Sample: 0 HQIC 21460.752
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.9974 0.003 -320.530 0.000 -1.004 -0.991
ar.L2 -0.9792 0.005 -202.874 0.000 -0.989 -0.970
ar.L3 -0.9442 0.007 -139.432 0.000 -0.957 -0.931
ar.L4 -0.9051 0.008 -115.448 0.000 -0.921 -0.890
ar.L5 -0.8718 0.009 -98.000 0.000 -0.889 -0.854
ar.L6 -0.8218 0.010 -82.963 0.000 -0.841 -0.802
ar.L7 -0.7926 0.011 -74.090 0.000 -0.814 -0.772
ar.L8 -0.7526 0.012 -64.364 0.000 -0.776 -0.730
ar.L9 -0.7206 0.012 -58.965 0.000 -0.745 -0.697
ar.L10 -0.6667 0.013 -52.090 0.000 -0.692 -0.642
ar.L11 -0.6182 0.014 -45.760 0.000 -0.645 -0.592
ar.L12 -0.6034 0.014 -43.542 0.000 -0.631 -0.576
ar.L13 -0.5682 0.014 -40.159 0.000 -0.596 -0.540
ar.L14 -0.5187 0.014 -36.369 0.000 -0.547 -0.491
ar.L15 -0.4934 0.015 -34.015 0.000 -0.522 -0.465
ar.L16 -0.4566 0.014 -31.635 0.000 -0.485 -0.428
ar.L17 -0.4141 0.014 -29.519 0.000 -0.442 -0.387
ar.L18 -0.3836 0.014 -27.244 0.000 -0.411 -0.356
ar.L19 -0.3370 0.014 -24.073 0.000 -0.364 -0.310
ar.L20 -0.3408 0.014 -24.640 0.000 -0.368 -0.314
ar.L21 -0.3360 0.014 -24.615 0.000 -0.363 -0.309
ar.L22 -0.3025 0.013 -23.594 0.000 -0.328 -0.277
ar.L23 -0.2842 0.012 -23.042 0.000 -0.308 -0.260
ar.L24 -0.2524 0.012 -21.074 0.000 -0.276 -0.229
ar.L25 -0.2077 0.011 -18.202 0.000 -0.230 -0.185
ar.L26 -0.1672 0.011 -15.902 0.000 -0.188 -0.147
ar.L27 -0.1584 0.010 -16.652 0.000 -0.177 -0.140
ar.L28 -0.1141 0.008 -14.024 0.000 -0.130 -0.098
ar.L29 -0.0691 0.007 -10.035 0.000 -0.083 -0.056
ar.L30 -0.0444 0.005 -9.122 0.000 -0.054 -0.035
ma.L1 -1.0000 0.008 -126.569 0.000 -1.015 -0.984
sigma2 0.1773 0.001 127.644 0.000 0.175 0.180
===================================================================================
Ljung-Box (L1) (Q): 0.07 Jarque-Bera (JB): 443976.76
Prob(Q): 0.80 Prob(JB): 0.00
Heteroskedasticity (H): 11.03 Skew: 0.48
Prob(H) (two-sided): 0.00 Kurtosis: 26.53
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 117.848886
19224 117.884004
19225 117.868083
19226 117.892985
19227 117.907172
19228 117.956668
19229 117.965660
19230 118.022084
19231 118.086217
19232 118.027670
Name: predicted_mean, dtype: float64
lower price upper price
19223 117.023576 118.674196
19224 116.715305 119.052703
19225 116.427303 119.308862
19226 116.209098 119.576872
19227 115.995836 119.818508
19228 115.829748 120.083589
19229 115.625204 120.306117
19230 115.475209 120.568959
19231 115.334291 120.838143
19232 115.073156 120.982185
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 19.0042 TKFEN
2021-12-27 10:00:00+03:00 19.1636 TKFEN
2021-12-27 11:00:00+03:00 18.8271 TKFEN
2021-12-27 12:00:00+03:00 18.7563 TKFEN
2021-12-27 13:00:00+03:00 18.7563 TKFEN
... ... ...
2024-01-11 14:00:00+03:00 37.5600 TKFEN
2024-01-11 15:00:00+03:00 37.1800 TKFEN
2024-01-11 16:00:00+03:00 37.4600 TKFEN
2024-01-11 17:00:00+03:00 37.5000 TKFEN
2024-01-11 18:00:00+03:00 37.4200 TKFEN
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 19.0042
2021-12-27 07:00:00+00:00 19.1636
2021-12-27 08:00:00+00:00 18.8271
2021-12-27 09:00:00+00:00 18.7563
2021-12-27 10:00:00+00:00 18.7563
2021-12-27 11:00:00+00:00 18.5791
2021-12-27 12:00:00+00:00 18.6146
2021-12-27 13:00:00+00:00 18.5791
2021-12-27 14:00:00+00:00 18.5082
2021-12-27 15:00:00+00:00 18.4374
2021-12-28 06:00:00+00:00 18.5968
2021-12-28 07:00:00+00:00 18.4906
2021-12-28 08:00:00+00:00 18.4020
2021-12-28 09:00:00+00:00 18.2781
2021-12-28 10:00:00+00:00 18.0478
2021-12-28 11:00:00+00:00 17.9592
2021-12-28 12:00:00+00:00 18.1364
2021-12-28 13:00:00+00:00 17.9060
2021-12-28 14:00:00+00:00 17.7113
2021-12-28 15:00:00+00:00 17.6581
2021-12-29 06:00:00+00:00 17.5696
2021-12-29 07:00:00+00:00 17.4987
2021-12-29 08:00:00+00:00 17.9415
2021-12-29 09:00:00+00:00 17.9769
2021-12-29 10:00:00+00:00 18.1718
2021-12-29 11:00:00+00:00 18.1009
2021-12-29 12:00:00+00:00 18.1895
2021-12-29 13:00:00+00:00 18.3666
2021-12-29 14:00:00+00:00 18.1895
2021-12-29 15:00:00+00:00 18.1895
2021-12-30 06:00:00+00:00 18.5614
2021-12-30 07:00:00+00:00 18.1186
2021-12-30 08:00:00+00:00 18.1718
2021-12-30 09:00:00+00:00 18.1009
2021-12-30 10:00:00+00:00 18.2249
2021-12-30 11:00:00+00:00 17.8884
2021-12-30 12:00:00+00:00 17.8530
2021-12-30 13:00:00+00:00 17.9060
2021-12-30 14:00:00+00:00 17.7998
2021-12-30 15:00:00+00:00 17.7820
2021-12-31 06:00:00+00:00 17.7998
2021-12-31 07:00:00+00:00 17.7291
2021-12-31 08:00:00+00:00 17.8706
2021-12-31 09:00:00+00:00 18.0832
2021-12-31 10:00:00+00:00 17.9415
2021-12-31 11:00:00+00:00 18.0832
2021-12-31 12:00:00+00:00 17.9769
2021-12-31 13:00:00+00:00 17.9769
2021-12-31 14:00:00+00:00 17.7820
2021-12-31 15:00:00+00:00 17.9238
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 36.860001
2024-01-10 10:00:00+00:00 36.900000
2024-01-10 10:30:00+00:00 36.880001
2024-01-10 11:00:00+00:00 37.020000
2024-01-10 11:30:00+00:00 36.980000
2024-01-10 12:00:00+00:00 36.620000
2024-01-10 12:30:00+00:00 36.939999
2024-01-10 13:00:00+00:00 37.140000
2024-01-10 13:30:00+00:00 37.259998
2024-01-10 14:00:00+00:00 37.260000
2024-01-10 15:00:00+00:00 37.100000
2024-01-11 06:00:00+00:00 37.060000
2024-01-11 06:30:00+00:00 37.560001
2024-01-11 07:00:00+00:00 37.580000
2024-01-11 07:30:00+00:00 37.619999
2024-01-11 08:00:00+00:00 37.560000
2024-01-11 08:30:00+00:00 37.639999
2024-01-11 09:00:00+00:00 37.620000
2024-01-11 09:30:00+00:00 37.639999
2024-01-11 10:00:00+00:00 37.520000
2024-01-11 10:30:00+00:00 37.360001
2024-01-11 11:00:00+00:00 37.560000
2024-01-11 11:30:00+00:00 37.419998
2024-01-11 12:00:00+00:00 37.180000
2024-01-11 12:30:00+00:00 37.560001
2024-01-11 13:00:00+00:00 37.460000
2024-01-11 13:30:00+00:00 37.400002
2024-01-11 14:00:00+00:00 37.500000
2024-01-11 14:30:00+00:00 37.419998
2024-01-11 15:00:00+00:00 37.420000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.060000
2018-01-02 11:00:00+03:00 -0.097400
2018-01-02 12:00:00+03:00 -0.112300
2018-01-02 13:00:00+03:00 0.000000
2018-01-02 14:00:00+03:00 0.007500
...
2024-01-11 13:00:00+00:00 -0.100001
2024-01-11 13:30:00+00:00 -0.059998
2024-01-11 14:00:00+00:00 0.099998
2024-01-11 14:30:00+00:00 -0.080002
2024-01-11 15:00:00+00:00 0.000002
Name: price, Length: 19223, dtype: float64
ADF Statistic: -21.454468857642006
p-value: 0.0
Critical Values: {'1%': -3.4306909715527216, '5%': -2.8616906970881657, '10%': -2.5668502120039105}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19224
Model: ARIMA(30, 1, 1) Log Likelihood -11958.641
Date: Thu, 11 Jan 2024 AIC 23981.283
Time: 22:14:18 BIC 24232.926
Sample: 0 HQIC 24063.771
- 19224
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.7061 0.147 -4.794 0.000 -0.995 -0.417
ar.L2 0.0396 0.130 0.304 0.761 -0.216 0.295
ar.L3 -0.0227 0.018 -1.247 0.212 -0.058 0.013
ar.L4 0.0173 0.009 1.945 0.052 -0.000 0.035
ar.L5 -0.0389 0.007 -5.684 0.000 -0.052 -0.025
ar.L6 0.0143 0.009 1.603 0.109 -0.003 0.032
ar.L7 -0.0337 0.007 -4.499 0.000 -0.048 -0.019
ar.L8 0.0092 0.009 1.007 0.314 -0.009 0.027
ar.L9 -0.0162 0.008 -2.143 0.032 -0.031 -0.001
ar.L10 0.0461 0.008 5.628 0.000 0.030 0.062
ar.L11 -0.0042 0.009 -0.464 0.643 -0.022 0.013
ar.L12 -0.0310 0.008 -4.060 0.000 -0.046 -0.016
ar.L13 -0.0159 0.007 -2.244 0.025 -0.030 -0.002
ar.L14 -0.0464 0.007 -6.931 0.000 -0.060 -0.033
ar.L15 -0.0301 0.010 -3.165 0.002 -0.049 -0.011
ar.L16 -0.1793 0.007 -24.472 0.000 -0.194 -0.165
ar.L17 -0.0726 0.028 -2.592 0.010 -0.128 -0.018
ar.L18 0.3952 0.016 24.388 0.000 0.363 0.427
ar.L19 0.4423 0.055 7.974 0.000 0.334 0.551
ar.L20 -0.1702 0.075 -2.266 0.023 -0.317 -0.023
ar.L21 -0.1786 0.013 -13.855 0.000 -0.204 -0.153
ar.L22 -0.0410 0.029 -1.411 0.158 -0.098 0.016
ar.L23 -0.0661 0.013 -5.238 0.000 -0.091 -0.041
ar.L24 0.0061 0.013 0.453 0.650 -0.020 0.032
ar.L25 -0.0298 0.007 -4.019 0.000 -0.044 -0.015
ar.L26 0.0310 0.009 3.488 0.000 0.014 0.048
ar.L27 -0.0237 0.009 -2.651 0.008 -0.041 -0.006
ar.L28 0.0018 0.008 0.216 0.829 -0.015 0.018
ar.L29 -0.0414 0.008 -5.297 0.000 -0.057 -0.026
ar.L30 0.0550 0.012 4.747 0.000 0.032 0.078
ma.L1 -0.1771 0.147 -1.203 0.229 -0.466 0.111
sigma2 0.2038 0.001 210.935 0.000 0.202 0.206
===================================================================================
Ljung-Box (L1) (Q): 0.04 Jarque-Bera (JB): 122009.29
Prob(Q): 0.84 Prob(JB): 0.00
Heteroskedasticity (H): 12.65 Skew: -0.93
Prob(H) (two-sided): 0.00 Kurtosis: 15.20
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224 37.574019
19225 37.717914
19226 37.575807
19227 37.583684
19228 37.585210
19229 37.573491
19230 37.602978
19231 37.522680
19232 37.481545
19233 37.569202
Name: predicted_mean, dtype: float64
lower price upper price
19224 36.689218 38.458821
19225 36.827098 38.608730
19226 36.448923 38.702690
19227 36.434597 38.732771
19228 36.284404 38.886016
19229 36.247304 38.899678
19230 36.153351 39.052605
19231 36.049939 38.995422
19232 35.899361 39.063728
19233 35.965116 39.173288
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 19.6507 TUPRS
2021-12-27 10:00:00+03:00 20.4990 TUPRS
2021-12-27 11:00:00+03:00 20.2331 TUPRS
2021-12-27 12:00:00+03:00 19.9926 TUPRS
2021-12-27 13:00:00+03:00 20.0052 TUPRS
... ... ...
2024-01-11 14:00:00+03:00 140.8000 TUPRS
2024-01-11 15:00:00+03:00 139.8000 TUPRS
2024-01-11 16:00:00+03:00 140.2000 TUPRS
2024-01-11 17:00:00+03:00 140.0000 TUPRS
2024-01-11 18:00:00+03:00 139.6000 TUPRS
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 19.6507
2021-12-27 07:00:00+00:00 20.4990
2021-12-27 08:00:00+00:00 20.2331
2021-12-27 09:00:00+00:00 19.9926
2021-12-27 10:00:00+00:00 20.0052
2021-12-27 11:00:00+00:00 19.7140
2021-12-27 12:00:00+00:00 19.6887
2021-12-27 13:00:00+00:00 19.6001
2021-12-27 14:00:00+00:00 19.3468
2021-12-27 15:00:00+00:00 19.3468
2021-12-28 06:00:00+00:00 19.7520
2021-12-28 07:00:00+00:00 19.7773
2021-12-28 08:00:00+00:00 19.5747
2021-12-28 09:00:00+00:00 19.5874
2021-12-28 10:00:00+00:00 19.5241
2021-12-28 11:00:00+00:00 19.4228
2021-12-28 12:00:00+00:00 19.3848
2021-12-28 13:00:00+00:00 19.2075
2021-12-28 14:00:00+00:00 18.9289
2021-12-28 15:00:00+00:00 18.9416
2021-12-29 06:00:00+00:00 18.9416
2021-12-29 07:00:00+00:00 18.8657
2021-12-29 08:00:00+00:00 19.2202
2021-12-29 09:00:00+00:00 19.3848
2021-12-29 10:00:00+00:00 19.5747
2021-12-29 11:00:00+00:00 19.6127
2021-12-29 12:00:00+00:00 19.6507
2021-12-29 13:00:00+00:00 19.8660
2021-12-29 14:00:00+00:00 19.8533
2021-12-29 15:00:00+00:00 19.7773
2021-12-30 06:00:00+00:00 20.0052
2021-12-30 07:00:00+00:00 19.9545
2021-12-30 08:00:00+00:00 19.9165
2021-12-30 09:00:00+00:00 19.8153
2021-12-30 10:00:00+00:00 19.7646
2021-12-30 11:00:00+00:00 19.5747
2021-12-30 12:00:00+00:00 19.6253
2021-12-30 13:00:00+00:00 19.6380
2021-12-30 14:00:00+00:00 19.4228
2021-12-30 15:00:00+00:00 19.3215
2021-12-31 06:00:00+00:00 19.3974
2021-12-31 07:00:00+00:00 19.6253
2021-12-31 08:00:00+00:00 19.8406
2021-12-31 09:00:00+00:00 19.7267
2021-12-31 10:00:00+00:00 19.4355
2021-12-31 11:00:00+00:00 19.6001
2021-12-31 12:00:00+00:00 19.4481
2021-12-31 13:00:00+00:00 19.3721
2021-12-31 14:00:00+00:00 19.2582
2021-12-31 15:00:00+00:00 19.5494
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 138.199997
2024-01-10 10:00:00+00:00 138.000000
2024-01-10 10:30:00+00:00 138.199997
2024-01-10 11:00:00+00:00 138.300000
2024-01-10 11:30:00+00:00 137.899994
2024-01-10 12:00:00+00:00 137.700000
2024-01-10 12:30:00+00:00 138.300003
2024-01-10 13:00:00+00:00 139.200000
2024-01-10 13:30:00+00:00 139.000000
2024-01-10 14:00:00+00:00 139.600000
2024-01-10 15:00:00+00:00 139.700000
2024-01-11 06:00:00+00:00 140.200000
2024-01-11 06:30:00+00:00 140.800003
2024-01-11 07:00:00+00:00 141.300000
2024-01-11 07:30:00+00:00 140.899994
2024-01-11 08:00:00+00:00 140.200000
2024-01-11 08:30:00+00:00 141.199997
2024-01-11 09:00:00+00:00 141.100000
2024-01-11 09:30:00+00:00 140.699997
2024-01-11 10:00:00+00:00 140.700000
2024-01-11 10:30:00+00:00 140.899994
2024-01-11 11:00:00+00:00 140.800000
2024-01-11 11:30:00+00:00 140.800003
2024-01-11 12:00:00+00:00 139.800000
2024-01-11 12:30:00+00:00 140.699997
2024-01-11 13:00:00+00:00 140.200000
2024-01-11 13:30:00+00:00 140.000000
2024-01-11 14:00:00+00:00 140.000000
2024-01-11 14:30:00+00:00 139.600006
2024-01-11 15:00:00+00:00 139.600000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.070300
2018-01-02 11:00:00+03:00 -0.020100
2018-01-02 12:00:00+03:00 0.010100
2018-01-02 13:00:00+03:00 0.020000
2018-01-02 14:00:00+03:00 0.030200
...
2024-01-11 13:00:00+00:00 -0.499997
2024-01-11 13:30:00+00:00 -0.200000
2024-01-11 14:00:00+00:00 0.000000
2024-01-11 14:30:00+00:00 -0.399994
2024-01-11 15:00:00+00:00 -0.000006
Name: price, Length: 19223, dtype: float64
ADF Statistic: -21.493290272406494
p-value: 0.0
Critical Values: {'1%': -3.4306909715527216, '5%': -2.8616906970881657, '10%': -2.5668502120039105}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19224
Model: ARIMA(30, 3, 1) Log Likelihood -27078.046
Date: Thu, 11 Jan 2024 AIC 54220.091
Time: 22:17:21 BIC 54471.732
Sample: 0 HQIC 54302.579
- 19224
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -2.0197 0.005 -416.771 0.000 -2.029 -2.010
ar.L2 -2.3160 0.012 -188.058 0.000 -2.340 -2.292
ar.L3 -2.3633 0.020 -120.347 0.000 -2.402 -2.325
ar.L4 -2.3033 0.027 -86.812 0.000 -2.355 -2.251
ar.L5 -2.2868 0.033 -68.525 0.000 -2.352 -2.221
ar.L6 -2.2112 0.039 -56.246 0.000 -2.288 -2.134
ar.L7 -2.1679 0.044 -48.836 0.000 -2.255 -2.081
ar.L8 -2.0757 0.049 -42.089 0.000 -2.172 -1.979
ar.L9 -2.0039 0.053 -37.474 0.000 -2.109 -1.899
ar.L10 -1.8534 0.057 -32.790 0.000 -1.964 -1.743
ar.L11 -1.7079 0.059 -29.096 0.000 -1.823 -1.593
ar.L12 -1.5251 0.060 -25.418 0.000 -1.643 -1.408
ar.L13 -1.3695 0.060 -22.832 0.000 -1.487 -1.252
ar.L14 -1.2926 0.059 -21.845 0.000 -1.409 -1.177
ar.L15 -1.2250 0.058 -20.998 0.000 -1.339 -1.111
ar.L16 -1.3320 0.058 -23.100 0.000 -1.445 -1.219
ar.L17 -1.4165 0.057 -24.815 0.000 -1.528 -1.305
ar.L18 -1.0238 0.057 -17.911 0.000 -1.136 -0.912
ar.L19 -0.2244 0.056 -3.988 0.000 -0.335 -0.114
ar.L20 0.0299 0.053 0.562 0.574 -0.074 0.134
ar.L21 0.0124 0.049 0.252 0.801 -0.084 0.109
ar.L22 0.0307 0.045 0.676 0.499 -0.058 0.120
ar.L23 -0.0206 0.041 -0.497 0.619 -0.102 0.060
ar.L24 -0.0188 0.038 -0.500 0.617 -0.092 0.055
ar.L25 -0.0353 0.034 -1.047 0.295 -0.101 0.031
ar.L26 0.0220 0.029 0.759 0.448 -0.035 0.079
ar.L27 0.0712 0.024 2.934 0.003 0.024 0.119
ar.L28 0.1303 0.020 6.654 0.000 0.092 0.169
ar.L29 0.1099 0.015 7.553 0.000 0.081 0.138
ar.L30 0.0823 0.007 12.147 0.000 0.069 0.096
ma.L1 -0.9566 0.004 -269.379 0.000 -0.964 -0.950
sigma2 1.0508 0.005 208.251 0.000 1.041 1.061
===================================================================================
Ljung-Box (L1) (Q): 29.90 Jarque-Bera (JB): 172261.02
Prob(Q): 0.00 Prob(JB): 0.00
Heteroskedasticity (H): 99.68 Skew: -0.43
Prob(H) (two-sided): 0.00 Kurtosis: 17.64
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224 140.178317
19225 140.336548
19226 140.131035
19227 139.507689
19228 139.849302
19229 140.153843
19230 139.528810
19231 139.486655
19232 139.486598
19233 139.646203
Name: predicted_mean, dtype: float64
lower price upper price
19224 138.169167 142.187467
19225 138.326833 142.346263
19226 137.599203 142.662867
19227 136.907643 142.107734
19228 136.888209 142.810394
19229 137.089550 143.218137
19230 136.105560 142.952060
19231 135.943273 143.030038
19232 135.563477 143.409718
19233 135.581079 143.711327
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 9.3416 TTKOM
2021-12-27 10:00:00+03:00 9.3416 TTKOM
2021-12-27 11:00:00+03:00 9.3061 TTKOM
2021-12-27 12:00:00+03:00 9.2795 TTKOM
2021-12-27 13:00:00+03:00 9.3061 TTKOM
... ... ...
2024-01-11 14:00:00+03:00 27.3200 TTKOM
2024-01-11 15:00:00+03:00 26.9600 TTKOM
2024-01-11 16:00:00+03:00 26.8400 TTKOM
2024-01-11 17:00:00+03:00 26.7800 TTKOM
2024-01-11 18:00:00+03:00 26.7800 TTKOM
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 9.3416
2021-12-27 07:00:00+00:00 9.3416
2021-12-27 08:00:00+00:00 9.3061
2021-12-27 09:00:00+00:00 9.2795
2021-12-27 10:00:00+00:00 9.3061
2021-12-27 11:00:00+00:00 9.1732
2021-12-27 12:00:00+00:00 9.0934
2021-12-27 13:00:00+00:00 9.1554
2021-12-27 14:00:00+00:00 9.1377
2021-12-27 15:00:00+00:00 9.1200
2021-12-28 06:00:00+00:00 9.1023
2021-12-28 07:00:00+00:00 9.2086
2021-12-28 08:00:00+00:00 9.1998
2021-12-28 09:00:00+00:00 9.1466
2021-12-28 10:00:00+00:00 9.1377
2021-12-28 11:00:00+00:00 9.0402
2021-12-28 12:00:00+00:00 9.0668
2021-12-28 13:00:00+00:00 8.9605
2021-12-28 14:00:00+00:00 8.7566
2021-12-28 15:00:00+00:00 8.7300
2021-12-29 06:00:00+00:00 8.7123
2021-12-29 07:00:00+00:00 8.7212
2021-12-29 08:00:00+00:00 8.7832
2021-12-29 09:00:00+00:00 8.8364
2021-12-29 10:00:00+00:00 8.8187
2021-12-29 11:00:00+00:00 8.7655
2021-12-29 12:00:00+00:00 8.8541
2021-12-29 13:00:00+00:00 8.8630
2021-12-29 14:00:00+00:00 8.9073
2021-12-29 15:00:00+00:00 8.8896
2021-12-30 06:00:00+00:00 8.9605
2021-12-30 07:00:00+00:00 8.8541
2021-12-30 08:00:00+00:00 8.8807
2021-12-30 09:00:00+00:00 8.8364
2021-12-30 10:00:00+00:00 8.8275
2021-12-30 11:00:00+00:00 8.7212
2021-12-30 12:00:00+00:00 8.7655
2021-12-30 13:00:00+00:00 8.7655
2021-12-30 14:00:00+00:00 8.6768
2021-12-30 15:00:00+00:00 8.6680
2021-12-31 06:00:00+00:00 8.6414
2021-12-31 07:00:00+00:00 8.6148
2021-12-31 08:00:00+00:00 8.6325
2021-12-31 09:00:00+00:00 8.6591
2021-12-31 10:00:00+00:00 8.5971
2021-12-31 11:00:00+00:00 8.6325
2021-12-31 12:00:00+00:00 8.5528
2021-12-31 13:00:00+00:00 8.5528
2021-12-31 14:00:00+00:00 8.5173
2021-12-31 15:00:00+00:00 8.5350
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 27.180000
2024-01-10 10:00:00+00:00 27.380000
2024-01-10 10:30:00+00:00 27.400000
2024-01-10 11:00:00+00:00 27.380000
2024-01-10 11:30:00+00:00 27.360001
2024-01-10 12:00:00+00:00 27.160000
2024-01-10 12:30:00+00:00 27.219999
2024-01-10 13:00:00+00:00 27.260000
2024-01-10 13:30:00+00:00 27.219999
2024-01-10 14:00:00+00:00 27.720000
2024-01-10 15:00:00+00:00 27.600000
2024-01-11 06:00:00+00:00 27.700000
2024-01-11 06:30:00+00:00 27.639999
2024-01-11 07:00:00+00:00 27.560000
2024-01-11 07:30:00+00:00 27.480000
2024-01-11 08:00:00+00:00 27.340000
2024-01-11 08:30:00+00:00 27.360001
2024-01-11 09:00:00+00:00 27.400000
2024-01-11 09:30:00+00:00 27.340000
2024-01-11 10:00:00+00:00 27.360000
2024-01-11 10:30:00+00:00 27.299999
2024-01-11 11:00:00+00:00 27.320000
2024-01-11 11:30:00+00:00 27.260000
2024-01-11 12:00:00+00:00 26.960000
2024-01-11 12:30:00+00:00 27.219999
2024-01-11 13:00:00+00:00 26.840000
2024-01-11 13:30:00+00:00 26.740000
2024-01-11 14:00:00+00:00 26.780000
2024-01-11 14:30:00+00:00 26.780001
2024-01-11 15:00:00+00:00 26.780000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 1.123000e-01
2018-01-02 11:00:00+03:00 0.000000e+00
2018-01-02 12:00:00+03:00 -1.610000e-02
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 2.410000e-02
...
2024-01-11 13:00:00+00:00 -3.799993e-01
2024-01-11 13:30:00+00:00 -1.000002e-01
2024-01-11 14:00:00+00:00 4.000023e-02
2024-01-11 14:30:00+00:00 6.866455e-07
2024-01-11 15:00:00+00:00 -6.866455e-07
Name: price, Length: 19223, dtype: float64
ADF Statistic: -20.3331771765092
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19224
Model: ARIMA(30, 3, 1) Log Likelihood 12036.130
Date: Thu, 11 Jan 2024 AIC -24008.261
Time: 22:20:30 BIC -23756.621
Sample: 0 HQIC -23925.773
- 19224
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.2045 0.004 -323.384 0.000 -1.212 -1.197
ar.L2 -1.1658 0.007 -157.840 0.000 -1.180 -1.151
ar.L3 -1.2179 0.010 -121.634 0.000 -1.237 -1.198
ar.L4 -1.1723 0.013 -91.897 0.000 -1.197 -1.147
ar.L5 -1.1992 0.015 -80.086 0.000 -1.229 -1.170
ar.L6 -1.1307 0.017 -66.630 0.000 -1.164 -1.097
ar.L7 -1.1569 0.018 -62.619 0.000 -1.193 -1.121
ar.L8 -1.0875 0.020 -54.420 0.000 -1.127 -1.048
ar.L9 -1.0710 0.021 -50.523 0.000 -1.113 -1.029
ar.L10 -0.9834 0.022 -44.176 0.000 -1.027 -0.940
ar.L11 -0.9440 0.023 -41.099 0.000 -0.989 -0.899
ar.L12 -0.8655 0.024 -36.763 0.000 -0.912 -0.819
ar.L13 -0.8157 0.024 -34.224 0.000 -0.862 -0.769
ar.L14 -0.7620 0.023 -32.437 0.000 -0.808 -0.716
ar.L15 -0.7125 0.023 -30.690 0.000 -0.758 -0.667
ar.L16 -0.7397 0.023 -32.345 0.000 -0.784 -0.695
ar.L17 -0.8757 0.023 -38.703 0.000 -0.920 -0.831
ar.L18 -0.4123 0.023 -18.005 0.000 -0.457 -0.367
ar.L19 -0.4375 0.022 -19.759 0.000 -0.481 -0.394
ar.L20 -0.5171 0.021 -24.232 0.000 -0.559 -0.475
ar.L21 -0.4826 0.021 -23.503 0.000 -0.523 -0.442
ar.L22 -0.4608 0.020 -23.505 0.000 -0.499 -0.422
ar.L23 -0.3805 0.019 -20.338 0.000 -0.417 -0.344
ar.L24 -0.3369 0.017 -19.359 0.000 -0.371 -0.303
ar.L25 -0.2544 0.016 -15.863 0.000 -0.286 -0.223
ar.L26 -0.1988 0.015 -13.636 0.000 -0.227 -0.170
ar.L27 -0.1476 0.013 -11.506 0.000 -0.173 -0.122
ar.L28 -0.1246 0.011 -11.674 0.000 -0.146 -0.104
ar.L29 -0.0796 0.009 -9.354 0.000 -0.096 -0.063
ar.L30 -0.0307 0.005 -6.040 0.000 -0.041 -0.021
ma.L1 -0.9583 0.003 -373.074 0.000 -0.963 -0.953
sigma2 0.0167 5.87e-05 284.317 0.000 0.017 0.017
===================================================================================
Ljung-Box (L1) (Q): 1.00 Jarque-Bera (JB): 363205.87
Prob(Q): 0.32 Prob(JB): 0.00
Heteroskedasticity (H): 16.47 Skew: 0.62
Prob(H) (two-sided): 0.00 Kurtosis: 24.26
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224 26.761062
19225 26.657897
19226 26.676648
19227 26.532380
19228 26.566860
19229 26.502741
19230 26.448061
19231 26.378880
19232 26.316081
19233 26.286939
Name: predicted_mean, dtype: float64
lower price upper price
19224 26.507847 27.014278
19225 26.327652 26.988141
19226 26.267934 27.085363
19227 26.063053 27.001707
19228 26.031233 27.102487
19229 25.910584 27.094899
19230 25.790631 27.105491
19231 25.665460 27.092300
19232 25.536983 27.095180
19233 25.447280 27.126599
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 18.8440 TCELL
2021-12-27 10:00:00+03:00 18.9601 TCELL
2021-12-27 11:00:00+03:00 18.7569 TCELL
2021-12-27 12:00:00+03:00 18.7182 TCELL
2021-12-27 13:00:00+03:00 18.7763 TCELL
... ... ...
2024-01-11 14:00:00+03:00 60.9000 TCELL
2024-01-11 15:00:00+03:00 60.6500 TCELL
2024-01-11 16:00:00+03:00 60.8000 TCELL
2024-01-11 17:00:00+03:00 60.8500 TCELL
2024-01-11 18:00:00+03:00 60.8500 TCELL
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 18.8440
2021-12-27 07:00:00+00:00 18.9601
2021-12-27 08:00:00+00:00 18.7569
2021-12-27 09:00:00+00:00 18.7182
2021-12-27 10:00:00+00:00 18.7763
2021-12-27 11:00:00+00:00 18.2536
2021-12-27 12:00:00+00:00 18.3310
2021-12-27 13:00:00+00:00 18.5343
2021-12-27 14:00:00+00:00 18.4085
2021-12-27 15:00:00+00:00 18.2923
2021-12-28 06:00:00+00:00 18.4569
2021-12-28 07:00:00+00:00 18.6988
2021-12-28 08:00:00+00:00 18.6020
2021-12-28 09:00:00+00:00 18.6311
2021-12-28 10:00:00+00:00 18.5633
2021-12-28 11:00:00+00:00 18.5246
2021-12-28 12:00:00+00:00 18.5343
2021-12-28 13:00:00+00:00 18.5246
2021-12-28 14:00:00+00:00 18.0988
2021-12-28 15:00:00+00:00 18.0020
2021-12-29 06:00:00+00:00 18.0020
2021-12-29 07:00:00+00:00 18.1665
2021-12-29 08:00:00+00:00 18.3891
2021-12-29 09:00:00+00:00 18.3891
2021-12-29 10:00:00+00:00 18.3794
2021-12-29 11:00:00+00:00 18.4085
2021-12-29 12:00:00+00:00 18.7375
2021-12-29 13:00:00+00:00 18.9408
2021-12-29 14:00:00+00:00 18.9698
2021-12-29 15:00:00+00:00 18.9698
2021-12-30 06:00:00+00:00 19.1053
2021-12-30 07:00:00+00:00 18.7956
2021-12-30 08:00:00+00:00 18.7763
2021-12-30 09:00:00+00:00 18.6988
2021-12-30 10:00:00+00:00 18.7375
2021-12-30 11:00:00+00:00 18.4085
2021-12-30 12:00:00+00:00 18.4569
2021-12-30 13:00:00+00:00 18.4665
2021-12-30 14:00:00+00:00 18.3698
2021-12-30 15:00:00+00:00 18.1859
2021-12-31 06:00:00+00:00 18.2536
2021-12-31 07:00:00+00:00 18.5633
2021-12-31 08:00:00+00:00 18.6988
2021-12-31 09:00:00+00:00 18.7375
2021-12-31 10:00:00+00:00 18.4569
2021-12-31 11:00:00+00:00 18.4472
2021-12-31 12:00:00+00:00 18.3504
2021-12-31 13:00:00+00:00 18.2827
2021-12-31 14:00:00+00:00 17.3535
2021-12-31 15:00:00+00:00 17.8665
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 61.200001
2024-01-10 10:00:00+00:00 61.550000
2024-01-10 10:30:00+00:00 61.500000
2024-01-10 11:00:00+00:00 61.850000
2024-01-10 11:30:00+00:00 61.549999
2024-01-10 12:00:00+00:00 61.350000
2024-01-10 12:30:00+00:00 61.500000
2024-01-10 13:00:00+00:00 61.850000
2024-01-10 13:30:00+00:00 61.549999
2024-01-10 14:00:00+00:00 61.600000
2024-01-10 15:00:00+00:00 61.500000
2024-01-11 06:00:00+00:00 61.850000
2024-01-11 06:30:00+00:00 61.599998
2024-01-11 07:00:00+00:00 61.150000
2024-01-11 07:30:00+00:00 61.250000
2024-01-11 08:00:00+00:00 60.900000
2024-01-11 08:30:00+00:00 61.049999
2024-01-11 09:00:00+00:00 61.000000
2024-01-11 09:30:00+00:00 60.849998
2024-01-11 10:00:00+00:00 60.700000
2024-01-11 10:30:00+00:00 60.599998
2024-01-11 11:00:00+00:00 60.900000
2024-01-11 11:30:00+00:00 61.049999
2024-01-11 12:00:00+00:00 60.650000
2024-01-11 12:30:00+00:00 61.250000
2024-01-11 13:00:00+00:00 60.800000
2024-01-11 13:30:00+00:00 60.750000
2024-01-11 14:00:00+00:00 60.850000
2024-01-11 14:30:00+00:00 60.849998
2024-01-11 15:00:00+00:00 60.850000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.039200
2018-01-02 11:00:00+03:00 -0.101800
2018-01-02 12:00:00+03:00 -0.007700
2018-01-02 13:00:00+03:00 0.054800
2018-01-02 14:00:00+03:00 0.054700
...
2024-01-11 13:00:00+00:00 -0.450000
2024-01-11 13:30:00+00:00 -0.050000
2024-01-11 14:00:00+00:00 0.100000
2024-01-11 14:30:00+00:00 -0.000002
2024-01-11 15:00:00+00:00 0.000002
Name: price, Length: 19223, dtype: float64
ADF Statistic: -21.803024085201393
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19224
Model: ARIMA(30, 1, 1) Log Likelihood 1917.768
Date: Thu, 11 Jan 2024 AIC -3771.536
Time: 22:23:17 BIC -3519.892
Sample: 0 HQIC -3689.048
- 19224
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.0724 0.007 -155.073 0.000 -1.086 -1.059
ar.L2 -0.1235 0.005 -24.672 0.000 -0.133 -0.114
ar.L3 -0.0317 0.005 -6.471 0.000 -0.041 -0.022
ar.L4 -0.0336 0.006 -5.707 0.000 -0.045 -0.022
ar.L5 -0.0257 0.007 -3.937 0.000 -0.038 -0.013
ar.L6 -0.0117 0.007 -1.738 0.082 -0.025 0.001
ar.L7 -0.0268 0.007 -3.761 0.000 -0.041 -0.013
ar.L8 -0.0276 0.007 -3.843 0.000 -0.042 -0.013
ar.L9 -0.0013 0.007 -0.189 0.850 -0.015 0.012
ar.L10 0.0048 0.007 0.678 0.498 -0.009 0.018
ar.L11 0.0253 0.007 3.795 0.000 0.012 0.038
ar.L12 0.0003 0.007 0.040 0.968 -0.013 0.014
ar.L13 -0.0039 0.007 -0.578 0.563 -0.017 0.009
ar.L14 0.0022 0.006 0.370 0.712 -0.010 0.014
ar.L15 0.0065 0.006 1.103 0.270 -0.005 0.018
ar.L16 -0.0197 0.006 -3.304 0.001 -0.031 -0.008
ar.L17 -0.0482 0.005 -9.013 0.000 -0.059 -0.038
ar.L18 0.1191 0.005 23.008 0.000 0.109 0.129
ar.L19 0.1058 0.004 23.736 0.000 0.097 0.115
ar.L20 -0.0719 0.005 -13.352 0.000 -0.082 -0.061
ar.L21 -0.0717 0.006 -12.934 0.000 -0.083 -0.061
ar.L22 -0.0346 0.006 -5.873 0.000 -0.046 -0.023
ar.L23 0.0011 0.006 0.172 0.863 -0.011 0.013
ar.L24 0.0143 0.006 2.377 0.017 0.003 0.026
ar.L25 0.0131 0.007 2.006 0.045 0.000 0.026
ar.L26 0.0121 0.007 1.737 0.082 -0.002 0.026
ar.L27 0.0038 0.007 0.541 0.588 -0.010 0.018
ar.L28 -0.0010 0.007 -0.145 0.884 -0.015 0.013
ar.L29 -0.0084 0.007 -1.167 0.243 -0.023 0.006
ar.L30 0.0301 0.006 5.448 0.000 0.019 0.041
ma.L1 0.8963 0.006 149.080 0.000 0.884 0.908
sigma2 0.0482 0.000 273.966 0.000 0.048 0.049
===================================================================================
Ljung-Box (L1) (Q): 0.24 Jarque-Bera (JB): 209404.58
Prob(Q): 0.62 Prob(JB): 0.00
Heteroskedasticity (H): 18.31 Skew: 0.63
Prob(H) (two-sided): 0.00 Kurtosis: 19.12
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224 60.846388
19225 60.764522
19226 60.826806
19227 60.798679
19228 60.823256
19229 60.859869
19230 60.809053
19231 60.832872
19232 60.764996
19233 60.879722
Name: predicted_mean, dtype: float64
lower price upper price
19224 60.416111 61.276665
19225 60.207033 61.322012
19226 60.150649 61.502962
19227 60.038149 61.559209
19228 59.977678 61.668834
19229 59.947941 61.771797
19230 59.826403 61.791703
19231 59.795556 61.870188
19232 59.668177 61.861816
19233 59.732729 62.026715
price short_name
timestamp
2021-12-27 09:00:00+03:00 4.78 HALKB
2021-12-27 10:00:00+03:00 4.80 HALKB
2021-12-27 11:00:00+03:00 4.78 HALKB
2021-12-27 12:00:00+03:00 4.78 HALKB
2021-12-27 13:00:00+03:00 4.78 HALKB
... ... ...
2024-01-11 14:00:00+03:00 13.15 HALKB
2024-01-11 15:00:00+03:00 12.99 HALKB
2024-01-11 16:00:00+03:00 12.98 HALKB
2024-01-11 17:00:00+03:00 13.02 HALKB
2024-01-11 18:00:00+03:00 13.02 HALKB
[5104 rows x 2 columns]
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 4.78
2021-12-27 07:00:00+00:00 4.80
2021-12-27 08:00:00+00:00 4.78
2021-12-27 09:00:00+00:00 4.78
2021-12-27 10:00:00+00:00 4.78
2021-12-27 11:00:00+00:00 4.74
2021-12-27 12:00:00+00:00 4.73
2021-12-27 13:00:00+00:00 4.74
2021-12-27 14:00:00+00:00 4.70
2021-12-27 15:00:00+00:00 4.70
2021-12-28 06:00:00+00:00 4.71
2021-12-28 07:00:00+00:00 4.67
2021-12-28 08:00:00+00:00 4.65
2021-12-28 09:00:00+00:00 4.66
2021-12-28 10:00:00+00:00 4.66
2021-12-28 11:00:00+00:00 4.64
2021-12-28 12:00:00+00:00 4.65
2021-12-28 13:00:00+00:00 4.64
2021-12-28 14:00:00+00:00 4.57
2021-12-28 15:00:00+00:00 4.58
2021-12-29 06:00:00+00:00 4.58
2021-12-29 07:00:00+00:00 4.55
2021-12-29 08:00:00+00:00 4.61
2021-12-29 09:00:00+00:00 4.58
2021-12-29 10:00:00+00:00 4.60
2021-12-29 11:00:00+00:00 4.59
2021-12-29 12:00:00+00:00 4.66
2021-12-29 13:00:00+00:00 4.65
2021-12-29 14:00:00+00:00 4.63
2021-12-29 15:00:00+00:00 4.64
2021-12-30 06:00:00+00:00 4.65
2021-12-30 07:00:00+00:00 4.65
2021-12-30 08:00:00+00:00 4.65
2021-12-30 09:00:00+00:00 4.64
2021-12-30 10:00:00+00:00 4.68
2021-12-30 11:00:00+00:00 4.63
2021-12-30 12:00:00+00:00 4.63
2021-12-30 13:00:00+00:00 4.62
2021-12-30 14:00:00+00:00 4.58
2021-12-30 15:00:00+00:00 4.58
2021-12-31 06:00:00+00:00 4.58
2021-12-31 07:00:00+00:00 4.58
2021-12-31 08:00:00+00:00 4.61
2021-12-31 09:00:00+00:00 4.60
2021-12-31 10:00:00+00:00 4.57
2021-12-31 11:00:00+00:00 4.56
2021-12-31 12:00:00+00:00 4.55
2021-12-31 13:00:00+00:00 4.55
2021-12-31 14:00:00+00:00 4.50
2021-12-31 15:00:00+00:00 4.51
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 13.28
2024-01-10 10:00:00+00:00 13.26
2024-01-10 10:30:00+00:00 13.33
2024-01-10 11:00:00+00:00 13.29
2024-01-10 11:30:00+00:00 13.29
2024-01-10 12:00:00+00:00 13.20
2024-01-10 12:30:00+00:00 13.22
2024-01-10 13:00:00+00:00 13.31
2024-01-10 13:30:00+00:00 13.29
2024-01-10 14:00:00+00:00 13.27
2024-01-10 15:00:00+00:00 13.22
2024-01-11 06:00:00+00:00 13.25
2024-01-11 06:30:00+00:00 13.39
2024-01-11 07:00:00+00:00 13.27
2024-01-11 07:30:00+00:00 13.20
2024-01-11 08:00:00+00:00 13.19
2024-01-11 08:30:00+00:00 13.22
2024-01-11 09:00:00+00:00 13.21
2024-01-11 09:30:00+00:00 13.18
2024-01-11 10:00:00+00:00 13.13
2024-01-11 10:30:00+00:00 13.13
2024-01-11 11:00:00+00:00 13.15
2024-01-11 11:30:00+00:00 13.11
2024-01-11 12:00:00+00:00 12.99
2024-01-11 12:30:00+00:00 13.07
2024-01-11 13:00:00+00:00 12.98
2024-01-11 13:30:00+00:00 12.96
2024-01-11 14:00:00+00:00 13.02
2024-01-11 14:30:00+00:00 13.02
2024-01-11 15:00:00+00:00 13.02
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 3.219000e-01
2018-01-02 11:00:00+03:00 -1.950000e-02
2018-01-02 12:00:00+03:00 -9.760000e-02
2018-01-02 13:00:00+03:00 1.950000e-02
2018-01-02 14:00:00+03:00 5.860000e-02
...
2024-01-11 13:00:00+00:00 -8.999969e-02
2024-01-11 13:30:00+00:00 -1.999996e-02
2024-01-11 14:00:00+00:00 5.999996e-02
2024-01-11 14:30:00+00:00 4.577637e-07
2024-01-11 15:00:00+00:00 -4.577637e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -20.568098322137963
p-value: 0.0
Critical Values: {'1%': -3.430690935998617, '5%': -2.8616906813754404, '10%': -2.5668502036402927}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 1, 1) Log Likelihood 22499.887
Date: Thu, 11 Jan 2024 AIC -44935.775
Time: 22:24:10 BIC -44684.133
Sample: 0 HQIC -44853.287
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.0020 0.325 -0.006 0.995 -0.638 0.634
ar.L2 0.0337 0.003 9.852 0.000 0.027 0.040
ar.L3 0.0162 0.012 1.325 0.185 -0.008 0.040
ar.L4 -0.0089 0.007 -1.221 0.222 -0.023 0.005
ar.L5 0.0081 0.006 1.433 0.152 -0.003 0.019
ar.L6 0.0211 0.006 3.615 0.000 0.010 0.033
ar.L7 -0.0064 0.009 -0.722 0.470 -0.024 0.011
ar.L8 0.0037 0.005 0.696 0.486 -0.007 0.014
ar.L9 -0.0018 0.006 -0.285 0.776 -0.014 0.011
ar.L10 0.0029 0.006 0.498 0.619 -0.008 0.014
ar.L11 0.0063 0.005 1.162 0.245 -0.004 0.017
ar.L12 -0.0068 0.006 -1.083 0.279 -0.019 0.005
ar.L13 -0.0109 0.005 -2.021 0.043 -0.021 -0.000
ar.L14 0.0143 0.006 2.268 0.023 0.002 0.027
ar.L15 0.0060 0.007 0.875 0.382 -0.007 0.019
ar.L16 -0.0119 0.004 -2.818 0.005 -0.020 -0.004
ar.L17 -0.0046 0.005 -0.848 0.396 -0.015 0.006
ar.L18 0.0101 0.003 2.942 0.003 0.003 0.017
ar.L19 0.0308 0.004 6.919 0.000 0.022 0.040
ar.L20 0.0191 0.011 1.737 0.082 -0.002 0.041
ar.L21 -0.0053 0.008 -0.674 0.501 -0.021 0.010
ar.L22 -0.0057 0.005 -1.167 0.243 -0.015 0.004
ar.L23 0.0011 0.005 0.207 0.836 -0.009 0.012
ar.L24 0.0077 0.005 1.484 0.138 -0.002 0.018
ar.L25 0.0148 0.006 2.381 0.017 0.003 0.027
ar.L26 -0.0135 0.008 -1.711 0.087 -0.029 0.002
ar.L27 0.0115 0.008 1.483 0.138 -0.004 0.027
ar.L28 0.0079 0.007 1.093 0.275 -0.006 0.022
ar.L29 -0.0011 0.007 -0.169 0.866 -0.014 0.012
ar.L30 0.0179 0.005 3.601 0.000 0.008 0.028
ma.L1 -0.0022 0.325 -0.007 0.995 -0.638 0.634
sigma2 0.0056 1.12e-05 502.973 0.000 0.006 0.006
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 4060904.23
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 3.97 Skew: 0.77
Prob(H) (two-sided): 0.00 Kurtosis: 74.19
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 13.024361
19224 13.027552
19225 13.028146
19226 13.019217
19227 13.018017
19228 13.020960
19229 13.023027
19230 13.020657
19231 13.016209
19232 13.018109
Name: predicted_mean, dtype: float64
lower price upper price
19223 12.877251 13.171470
19224 12.819950 13.235154
19225 12.771176 13.285115
19226 12.719739 13.318695
19227 12.681885 13.354148
19228 12.651234 13.390687
19229 12.621330 13.424723
19230 12.589685 13.451630
19231 12.557550 13.474867
19232 12.533426 13.502792
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 3.1299 ISCTR
2021-12-27 10:00:00+03:00 3.1702 ISCTR
2021-12-27 11:00:00+03:00 3.1823 ISCTR
2021-12-27 12:00:00+03:00 3.1984 ISCTR
2021-12-27 13:00:00+03:00 3.1864 ISCTR
... ... ...
2024-01-11 14:00:00+03:00 25.7000 ISCTR
2024-01-11 15:00:00+03:00 25.6600 ISCTR
2024-01-11 16:00:00+03:00 25.6400 ISCTR
2024-01-11 17:00:00+03:00 25.7400 ISCTR
2024-01-11 18:00:00+03:00 25.7200 ISCTR
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 3.1299
2021-12-27 07:00:00+00:00 3.1702
2021-12-27 08:00:00+00:00 3.1823
2021-12-27 09:00:00+00:00 3.1984
2021-12-27 10:00:00+00:00 3.1864
2021-12-27 11:00:00+00:00 3.1621
2021-12-27 12:00:00+00:00 3.1823
2021-12-27 13:00:00+00:00 3.1904
2021-12-27 14:00:00+00:00 3.2146
2021-12-27 15:00:00+00:00 3.2146
2021-12-28 06:00:00+00:00 3.1943
2021-12-28 07:00:00+00:00 3.1056
2021-12-28 08:00:00+00:00 3.0815
2021-12-28 09:00:00+00:00 3.0734
2021-12-28 10:00:00+00:00 3.0774
2021-12-28 11:00:00+00:00 3.0653
2021-12-28 12:00:00+00:00 3.0734
2021-12-28 13:00:00+00:00 3.0492
2021-12-28 14:00:00+00:00 3.0089
2021-12-28 15:00:00+00:00 3.0089
2021-12-29 06:00:00+00:00 2.9766
2021-12-29 07:00:00+00:00 2.9484
2021-12-29 08:00:00+00:00 3.0008
2021-12-29 09:00:00+00:00 2.9968
2021-12-29 10:00:00+00:00 3.0008
2021-12-29 11:00:00+00:00 3.0049
2021-12-29 12:00:00+00:00 3.0250
2021-12-29 13:00:00+00:00 3.0411
2021-12-29 14:00:00+00:00 3.0169
2021-12-29 15:00:00+00:00 3.0089
2021-12-30 06:00:00+00:00 3.0209
2021-12-30 07:00:00+00:00 3.0452
2021-12-30 08:00:00+00:00 3.0613
2021-12-30 09:00:00+00:00 3.0573
2021-12-30 10:00:00+00:00 3.0653
2021-12-30 11:00:00+00:00 3.0330
2021-12-30 12:00:00+00:00 3.0330
2021-12-30 13:00:00+00:00 3.0371
2021-12-30 14:00:00+00:00 2.9887
2021-12-30 15:00:00+00:00 2.9766
2021-12-31 06:00:00+00:00 2.9928
2021-12-31 07:00:00+00:00 2.9847
2021-12-31 08:00:00+00:00 2.9968
2021-12-31 09:00:00+00:00 3.0049
2021-12-31 10:00:00+00:00 2.9766
2021-12-31 11:00:00+00:00 2.9725
2021-12-31 12:00:00+00:00 2.9565
2021-12-31 13:00:00+00:00 2.9282
2021-12-31 14:00:00+00:00 2.8959
2021-12-31 15:00:00+00:00 2.9000
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 25.100000
2024-01-10 10:00:00+00:00 25.120000
2024-01-10 10:30:00+00:00 25.260000
2024-01-10 11:00:00+00:00 25.260000
2024-01-10 11:30:00+00:00 25.260000
2024-01-10 12:00:00+00:00 25.040000
2024-01-10 12:30:00+00:00 25.219999
2024-01-10 13:00:00+00:00 25.520000
2024-01-10 13:30:00+00:00 25.600000
2024-01-10 14:00:00+00:00 25.740000
2024-01-10 15:00:00+00:00 25.700000
2024-01-11 06:00:00+00:00 25.860000
2024-01-11 06:30:00+00:00 25.760000
2024-01-11 07:00:00+00:00 25.460000
2024-01-11 07:30:00+00:00 25.480000
2024-01-11 08:00:00+00:00 25.500000
2024-01-11 08:30:00+00:00 25.540001
2024-01-11 09:00:00+00:00 25.580000
2024-01-11 09:30:00+00:00 25.680000
2024-01-11 10:00:00+00:00 25.660000
2024-01-11 10:30:00+00:00 25.879999
2024-01-11 11:00:00+00:00 25.700000
2024-01-11 11:30:00+00:00 25.700001
2024-01-11 12:00:00+00:00 25.660000
2024-01-11 12:30:00+00:00 25.820000
2024-01-11 13:00:00+00:00 25.640000
2024-01-11 13:30:00+00:00 25.660000
2024-01-11 14:00:00+00:00 25.740000
2024-01-11 14:30:00+00:00 25.719999
2024-01-11 15:00:00+00:00 25.720000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 1.130000e-02
2018-01-02 11:00:00+03:00 0.000000e+00
2018-01-02 12:00:00+03:00 -7.500000e-03
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 3.000000e-02
...
2024-01-11 13:00:00+00:00 -1.799997e-01
2024-01-11 13:30:00+00:00 1.999985e-02
2024-01-11 14:00:00+00:00 8.000015e-02
2024-01-11 14:30:00+00:00 -2.000069e-02
2024-01-11 15:00:00+00:00 6.866455e-07
Name: price, Length: 19223, dtype: float64
ADF Statistic: -20.20767182047305
p-value: 0.0
Critical Values: {'1%': -3.4306910071142425, '5%': -2.8616907128041684, '10%': -2.5668502203692722}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19224
Model: ARIMA(30, 1, 1) Log Likelihood 12517.087
Date: Thu, 11 Jan 2024 AIC -24970.173
Time: 22:26:46 BIC -24718.530
Sample: 0 HQIC -24887.685
- 19224
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.5987 0.094 -6.400 0.000 -0.782 -0.415
ar.L2 0.0870 0.060 1.445 0.148 -0.031 0.205
ar.L3 -0.0670 0.008 -8.828 0.000 -0.082 -0.052
ar.L4 0.0010 0.008 0.130 0.896 -0.015 0.017
ar.L5 -0.0458 0.005 -8.624 0.000 -0.056 -0.035
ar.L6 0.0235 0.007 3.267 0.001 0.009 0.038
ar.L7 -0.0191 0.006 -2.946 0.003 -0.032 -0.006
ar.L8 0.0233 0.007 3.405 0.001 0.010 0.037
ar.L9 -0.0138 0.007 -1.980 0.048 -0.028 -0.000
ar.L10 0.0542 0.006 8.447 0.000 0.042 0.067
ar.L11 0.0173 0.008 2.156 0.031 0.002 0.033
ar.L12 -0.0118 0.007 -1.676 0.094 -0.026 0.002
ar.L13 0.0156 0.006 2.670 0.008 0.004 0.027
ar.L14 0.0065 0.005 1.194 0.232 -0.004 0.017
ar.L15 0.0159 0.005 3.230 0.001 0.006 0.026
ar.L16 -0.1312 0.005 -27.847 0.000 -0.140 -0.122
ar.L17 -0.0695 0.013 -5.499 0.000 -0.094 -0.045
ar.L18 0.2758 0.008 35.756 0.000 0.261 0.291
ar.L19 0.2992 0.026 11.665 0.000 0.249 0.350
ar.L20 -0.1516 0.029 -5.180 0.000 -0.209 -0.094
ar.L21 -0.0675 0.014 -4.935 0.000 -0.094 -0.041
ar.L22 0.0194 0.009 2.275 0.023 0.003 0.036
ar.L23 -0.0415 0.005 -8.497 0.000 -0.051 -0.032
ar.L24 0.0127 0.007 1.926 0.054 -0.000 0.026
ar.L25 -0.0182 0.006 -3.105 0.002 -0.030 -0.007
ar.L26 0.0478 0.006 7.896 0.000 0.036 0.060
ar.L27 -0.0097 0.008 -1.210 0.226 -0.025 0.006
ar.L28 0.0072 0.007 1.090 0.276 -0.006 0.020
ar.L29 -0.0424 0.006 -7.055 0.000 -0.054 -0.031
ar.L30 0.0604 0.008 7.436 0.000 0.045 0.076
ma.L1 -0.0420 0.094 -0.449 0.653 -0.226 0.142
sigma2 0.0160 6.43e-05 248.687 0.000 0.016 0.016
===================================================================================
Ljung-Box (L1) (Q): 0.06 Jarque-Bera (JB): 243151.50
Prob(Q): 0.80 Prob(JB): 0.00
Heteroskedasticity (H): 99.96 Skew: 0.22
Prob(H) (two-sided): 0.00 Kurtosis: 20.42
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224 25.765533
19225 25.573763
19226 25.613909
19227 25.642792
19228 25.667359
19229 25.650499
19230 25.670518
19231 25.702894
19232 25.735713
19233 25.726259
Name: predicted_mean, dtype: float64
lower price upper price
19224 25.517605 26.013460
19225 25.310319 25.837207
19226 25.279649 25.948168
19227 25.292285 25.993299
19228 25.270304 26.064415
19229 25.238497 26.062502
19230 25.219789 26.121247
19231 25.238170 26.167618
19232 25.236425 26.235001
19233 25.213990 26.238528
price short_name
timestamp
2021-12-27 09:00:00+03:00 3.86 VAKBN
2021-12-27 10:00:00+03:00 3.87 VAKBN
2021-12-27 11:00:00+03:00 3.86 VAKBN
2021-12-27 12:00:00+03:00 3.86 VAKBN
2021-12-27 13:00:00+03:00 3.85 VAKBN
... ... ...
2024-01-11 14:00:00+03:00 14.70 VAKBN
2024-01-11 15:00:00+03:00 14.55 VAKBN
2024-01-11 16:00:00+03:00 14.61 VAKBN
2024-01-11 17:00:00+03:00 14.68 VAKBN
2024-01-11 18:00:00+03:00 14.60 VAKBN
[5104 rows x 2 columns]
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 3.86
2021-12-27 07:00:00+00:00 3.87
2021-12-27 08:00:00+00:00 3.86
2021-12-27 09:00:00+00:00 3.86
2021-12-27 10:00:00+00:00 3.85
2021-12-27 11:00:00+00:00 3.83
2021-12-27 12:00:00+00:00 3.84
2021-12-27 13:00:00+00:00 3.86
2021-12-27 14:00:00+00:00 3.83
2021-12-27 15:00:00+00:00 3.83
2021-12-28 06:00:00+00:00 3.84
2021-12-28 07:00:00+00:00 3.78
2021-12-28 08:00:00+00:00 3.78
2021-12-28 09:00:00+00:00 3.77
2021-12-28 10:00:00+00:00 3.77
2021-12-28 11:00:00+00:00 3.77
2021-12-28 12:00:00+00:00 3.78
2021-12-28 13:00:00+00:00 3.76
2021-12-28 14:00:00+00:00 3.68
2021-12-28 15:00:00+00:00 3.69
2021-12-29 06:00:00+00:00 3.65
2021-12-29 07:00:00+00:00 3.66
2021-12-29 08:00:00+00:00 3.74
2021-12-29 09:00:00+00:00 3.73
2021-12-29 10:00:00+00:00 3.73
2021-12-29 11:00:00+00:00 3.74
2021-12-29 12:00:00+00:00 3.75
2021-12-29 13:00:00+00:00 3.75
2021-12-29 14:00:00+00:00 3.74
2021-12-29 15:00:00+00:00 3.74
2021-12-30 06:00:00+00:00 3.75
2021-12-30 07:00:00+00:00 3.75
2021-12-30 08:00:00+00:00 3.76
2021-12-30 09:00:00+00:00 3.75
2021-12-30 10:00:00+00:00 3.77
2021-12-30 11:00:00+00:00 3.74
2021-12-30 12:00:00+00:00 3.74
2021-12-30 13:00:00+00:00 3.75
2021-12-30 14:00:00+00:00 3.70
2021-12-30 15:00:00+00:00 3.71
2021-12-31 06:00:00+00:00 3.70
2021-12-31 07:00:00+00:00 3.69
2021-12-31 08:00:00+00:00 3.71
2021-12-31 09:00:00+00:00 3.69
2021-12-31 10:00:00+00:00 3.69
2021-12-31 11:00:00+00:00 3.67
2021-12-31 12:00:00+00:00 3.67
2021-12-31 13:00:00+00:00 3.65
2021-12-31 14:00:00+00:00 3.63
2021-12-31 15:00:00+00:00 3.68
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 14.75
2024-01-10 10:00:00+00:00 14.73
2024-01-10 10:30:00+00:00 14.77
2024-01-10 11:00:00+00:00 14.77
2024-01-10 11:30:00+00:00 14.76
2024-01-10 12:00:00+00:00 14.62
2024-01-10 12:30:00+00:00 14.71
2024-01-10 13:00:00+00:00 14.85
2024-01-10 13:30:00+00:00 14.87
2024-01-10 14:00:00+00:00 14.86
2024-01-10 15:00:00+00:00 14.81
2024-01-11 06:00:00+00:00 14.88
2024-01-11 06:30:00+00:00 14.98
2024-01-11 07:00:00+00:00 14.82
2024-01-11 07:30:00+00:00 14.73
2024-01-11 08:00:00+00:00 14.72
2024-01-11 08:30:00+00:00 14.75
2024-01-11 09:00:00+00:00 14.74
2024-01-11 09:30:00+00:00 14.73
2024-01-11 10:00:00+00:00 14.67
2024-01-11 10:30:00+00:00 14.73
2024-01-11 11:00:00+00:00 14.70
2024-01-11 11:30:00+00:00 14.66
2024-01-11 12:00:00+00:00 14.55
2024-01-11 12:30:00+00:00 14.67
2024-01-11 13:00:00+00:00 14.61
2024-01-11 13:30:00+00:00 14.63
2024-01-11 14:00:00+00:00 14.68
2024-01-11 14:30:00+00:00 14.60
2024-01-11 15:00:00+00:00 14.60
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 1.085000e-01
2018-01-02 11:00:00+03:00 7.880000e-02
2018-01-02 12:00:00+03:00 0.000000e+00
2018-01-02 13:00:00+03:00 9.900000e-03
2018-01-02 14:00:00+03:00 3.940000e-02
...
2024-01-11 13:00:00+00:00 -6.000008e-02
2024-01-11 13:30:00+00:00 2.000011e-02
2024-01-11 14:00:00+00:00 4.999989e-02
2024-01-11 14:30:00+00:00 -7.999962e-02
2024-01-11 15:00:00+00:00 -3.814697e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -19.272336007438795
p-value: 0.0
Critical Values: {'1%': -3.4306909715527216, '5%': -2.8616906970881657, '10%': -2.5668502120039105}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 1, 1) Log Likelihood 24301.619
Date: Thu, 11 Jan 2024 AIC -48539.238
Time: 22:27:25 BIC -48287.596
Sample: 0 HQIC -48456.750
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.0305 0.291 -0.105 0.917 -0.602 0.541
ar.L2 0.0173 0.018 0.976 0.329 -0.017 0.052
ar.L3 0.0131 0.006 2.154 0.031 0.001 0.025
ar.L4 0.0106 0.006 1.802 0.072 -0.001 0.022
ar.L5 0.0138 0.005 2.521 0.012 0.003 0.025
ar.L6 0.0144 0.006 2.228 0.026 0.002 0.027
ar.L7 -0.0028 0.007 -0.397 0.691 -0.017 0.011
ar.L8 -0.0033 0.005 -0.608 0.543 -0.014 0.007
ar.L9 0.0002 0.005 0.035 0.972 -0.010 0.011
ar.L10 0.0019 0.006 0.348 0.728 -0.009 0.013
ar.L11 0.0030 0.005 0.618 0.537 -0.006 0.012
ar.L12 -0.0035 0.005 -0.670 0.503 -0.014 0.007
ar.L13 -0.0108 0.005 -2.248 0.025 -0.020 -0.001
ar.L14 0.0145 0.005 2.707 0.007 0.004 0.025
ar.L15 0.0145 0.007 2.199 0.028 0.002 0.027
ar.L16 0.0109 0.005 2.130 0.033 0.001 0.021
ar.L17 -0.0096 0.006 -1.660 0.097 -0.021 0.002
ar.L18 0.0156 0.003 4.714 0.000 0.009 0.022
ar.L19 0.0606 0.005 11.816 0.000 0.051 0.071
ar.L20 0.0125 0.018 0.683 0.495 -0.023 0.048
ar.L21 -0.0247 0.006 -4.445 0.000 -0.036 -0.014
ar.L22 0.0032 0.008 0.378 0.705 -0.013 0.020
ar.L23 -0.0126 0.005 -2.770 0.006 -0.022 -0.004
ar.L24 0.0065 0.007 0.942 0.346 -0.007 0.020
ar.L25 0.0259 0.005 5.593 0.000 0.017 0.035
ar.L26 0.0040 0.010 0.419 0.675 -0.015 0.023
ar.L27 -0.0030 0.006 -0.468 0.640 -0.016 0.010
ar.L28 0.0035 0.005 0.646 0.519 -0.007 0.014
ar.L29 -0.0029 0.005 -0.576 0.565 -0.013 0.007
ar.L30 0.0169 0.005 3.170 0.002 0.006 0.027
ma.L1 -0.0294 0.292 -0.101 0.920 -0.601 0.542
sigma2 0.0047 1.02e-05 459.726 0.000 0.005 0.005
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 3646482.24
Prob(Q): 1.00 Prob(JB): 0.00
Heteroskedasticity (H): 7.81 Skew: 0.70
Prob(H) (two-sided): 0.00 Kurtosis: 70.46
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 14.600949
19224 14.608956
19225 14.602585
19226 14.591470
19227 14.592539
19228 14.592248
19229 14.598370
19230 14.597028
19231 14.590778
19232 14.589439
Name: predicted_mean, dtype: float64
lower price upper price
19223 14.467005 14.734893
19224 14.425117 14.792795
19225 14.378299 14.826870
19226 14.332224 14.850717
19227 14.301922 14.883155
19228 14.272591 14.911904
19229 14.251394 14.945346
19230 14.224894 14.969161
19231 14.195175 14.986380
19232 14.171644 15.007234
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 24.94 VESTL
2021-12-27 10:00:00+03:00 25.44 VESTL
2021-12-27 11:00:00+03:00 25.16 VESTL
2021-12-27 12:00:00+03:00 24.98 VESTL
2021-12-27 13:00:00+03:00 25.08 VESTL
... ... ...
2024-01-11 14:00:00+03:00 48.16 VESTL
2024-01-11 15:00:00+03:00 47.92 VESTL
2024-01-11 16:00:00+03:00 47.90 VESTL
2024-01-11 17:00:00+03:00 48.20 VESTL
2024-01-11 18:00:00+03:00 48.38 VESTL
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 24.94
2021-12-27 07:00:00+00:00 25.44
2021-12-27 08:00:00+00:00 25.16
2021-12-27 09:00:00+00:00 24.98
2021-12-27 10:00:00+00:00 25.08
2021-12-27 11:00:00+00:00 24.68
2021-12-27 12:00:00+00:00 24.62
2021-12-27 13:00:00+00:00 24.70
2021-12-27 14:00:00+00:00 24.72
2021-12-27 15:00:00+00:00 24.68
2021-12-28 06:00:00+00:00 24.84
2021-12-28 07:00:00+00:00 24.74
2021-12-28 08:00:00+00:00 24.50
2021-12-28 09:00:00+00:00 24.42
2021-12-28 10:00:00+00:00 24.32
2021-12-28 11:00:00+00:00 24.26
2021-12-28 12:00:00+00:00 24.26
2021-12-28 13:00:00+00:00 24.10
2021-12-28 14:00:00+00:00 23.84
2021-12-28 15:00:00+00:00 23.80
2021-12-29 06:00:00+00:00 23.88
2021-12-29 07:00:00+00:00 23.66
2021-12-29 08:00:00+00:00 24.08
2021-12-29 09:00:00+00:00 24.16
2021-12-29 10:00:00+00:00 24.16
2021-12-29 11:00:00+00:00 24.16
2021-12-29 12:00:00+00:00 24.32
2021-12-29 13:00:00+00:00 24.36
2021-12-29 14:00:00+00:00 24.32
2021-12-29 15:00:00+00:00 24.28
2021-12-30 06:00:00+00:00 24.68
2021-12-30 07:00:00+00:00 24.38
2021-12-30 08:00:00+00:00 24.58
2021-12-30 09:00:00+00:00 24.48
2021-12-30 10:00:00+00:00 24.46
2021-12-30 11:00:00+00:00 24.18
2021-12-30 12:00:00+00:00 24.20
2021-12-30 13:00:00+00:00 24.18
2021-12-30 14:00:00+00:00 24.18
2021-12-30 15:00:00+00:00 24.10
2021-12-31 06:00:00+00:00 24.14
2021-12-31 07:00:00+00:00 24.04
2021-12-31 08:00:00+00:00 24.26
2021-12-31 09:00:00+00:00 24.40
2021-12-31 10:00:00+00:00 24.40
2021-12-31 11:00:00+00:00 24.50
2021-12-31 12:00:00+00:00 24.48
2021-12-31 13:00:00+00:00 24.66
2021-12-31 14:00:00+00:00 24.94
2021-12-31 15:00:00+00:00 25.04
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 47.680000
2024-01-10 10:00:00+00:00 47.680000
2024-01-10 10:30:00+00:00 47.639999
2024-01-10 11:00:00+00:00 47.640000
2024-01-10 11:30:00+00:00 47.680000
2024-01-10 12:00:00+00:00 47.620000
2024-01-10 12:30:00+00:00 47.840000
2024-01-10 13:00:00+00:00 48.620000
2024-01-10 13:30:00+00:00 48.459999
2024-01-10 14:00:00+00:00 48.680000
2024-01-10 15:00:00+00:00 48.640000
2024-01-11 06:00:00+00:00 48.640000
2024-01-11 06:30:00+00:00 48.480000
2024-01-11 07:00:00+00:00 48.400000
2024-01-11 07:30:00+00:00 48.279999
2024-01-11 08:00:00+00:00 48.240000
2024-01-11 08:30:00+00:00 48.380001
2024-01-11 09:00:00+00:00 48.320000
2024-01-11 09:30:00+00:00 48.259998
2024-01-11 10:00:00+00:00 48.240000
2024-01-11 10:30:00+00:00 48.180000
2024-01-11 11:00:00+00:00 48.160000
2024-01-11 11:30:00+00:00 48.200001
2024-01-11 12:00:00+00:00 47.920000
2024-01-11 12:30:00+00:00 48.220001
2024-01-11 13:00:00+00:00 47.900000
2024-01-11 13:30:00+00:00 47.939999
2024-01-11 14:00:00+00:00 48.200000
2024-01-11 14:30:00+00:00 48.380001
2024-01-11 15:00:00+00:00 48.380000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 0.173300
2018-01-02 11:00:00+03:00 0.315000
2018-01-02 12:00:00+03:00 0.039300
2018-01-02 13:00:00+03:00 0.000000
2018-01-02 14:00:00+03:00 0.322900
...
2024-01-11 13:00:00+00:00 -0.320001
2024-01-11 13:30:00+00:00 0.039999
2024-01-11 14:00:00+00:00 0.260001
2024-01-11 14:30:00+00:00 0.180001
2024-01-11 15:00:00+00:00 -0.000001
Name: price, Length: 19222, dtype: float64
ADF Statistic: -23.075424884400462
p-value: 0.0
Critical Values: {'1%': -3.430690918224345, '5%': -2.861690673520306, '10%': -2.566850199459138}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting. self._init_dates(dates, freq)
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19223
Model: ARIMA(30, 1, 1) Log Likelihood -3308.365
Date: Thu, 11 Jan 2024 AIC 6680.731
Time: 22:27:57 BIC 6932.373
Sample: 0 HQIC 6763.219
- 19223
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.0035 1.277 -0.003 0.998 -2.507 2.500
ar.L2 -0.0384 0.010 -3.931 0.000 -0.058 -0.019
ar.L3 0.0003 0.049 0.007 0.994 -0.096 0.097
ar.L4 0.0007 0.004 0.194 0.847 -0.007 0.008
ar.L5 0.0010 0.004 0.236 0.813 -0.007 0.009
ar.L6 9.64e-05 0.004 0.022 0.982 -0.008 0.009
ar.L7 0.0111 0.004 2.683 0.007 0.003 0.019
ar.L8 0.0102 0.015 0.680 0.496 -0.019 0.040
ar.L9 0.0291 0.014 2.139 0.032 0.002 0.056
ar.L10 0.0030 0.037 0.080 0.936 -0.070 0.076
ar.L11 0.0073 0.005 1.342 0.179 -0.003 0.018
ar.L12 -0.0169 0.010 -1.618 0.106 -0.037 0.004
ar.L13 0.0232 0.022 1.041 0.298 -0.021 0.067
ar.L14 0.0200 0.030 0.670 0.503 -0.038 0.078
ar.L15 0.0106 0.026 0.411 0.681 -0.040 0.061
ar.L16 -0.0107 0.014 -0.744 0.457 -0.039 0.017
ar.L17 0.0088 0.014 0.624 0.533 -0.019 0.037
ar.L18 0.0141 0.012 1.216 0.224 -0.009 0.037
ar.L19 0.0045 0.018 0.246 0.806 -0.032 0.041
ar.L20 -0.0182 0.007 -2.603 0.009 -0.032 -0.004
ar.L21 -0.0013 0.024 -0.056 0.955 -0.047 0.045
ar.L22 -0.0050 0.004 -1.195 0.232 -0.013 0.003
ar.L23 -0.0080 0.008 -1.022 0.307 -0.023 0.007
ar.L24 -0.0138 0.011 -1.263 0.207 -0.035 0.008
ar.L25 -0.0096 0.018 -0.530 0.596 -0.045 0.026
ar.L26 -0.0106 0.013 -0.828 0.408 -0.036 0.015
ar.L27 -0.0067 0.014 -0.466 0.641 -0.035 0.021
ar.L28 -0.0178 0.009 -1.885 0.059 -0.036 0.001
ar.L29 0.0012 0.023 0.049 0.961 -0.045 0.047
ar.L30 0.0034 0.004 0.856 0.392 -0.004 0.011
ma.L1 -0.0038 1.277 -0.003 0.998 -2.506 2.499
sigma2 0.0826 0.000 381.870 0.000 0.082 0.083
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 2142776.40
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 25.80 Skew: 1.38
Prob(H) (two-sided): 0.00 Kurtosis: 54.65
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223 48.361908
19224 48.347745
19225 48.328307
19226 48.324971
19227 48.312104
19228 48.311258
19229 48.311661
19230 48.327682
19231 48.328404
19232 48.328275
Name: predicted_mean, dtype: float64
lower price upper price
19223 47.798593 48.925222
19224 47.554004 49.141487
19225 47.369654 49.286960
19226 47.225670 49.424273
19227 47.087667 49.536541
19228 46.973126 49.649390
19229 46.868770 49.754552
19230 46.784946 49.870417
19231 46.689995 49.966814
19232 46.594361 50.062188
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
price short_name
timestamp
2021-12-27 09:00:00+03:00 3.1711 YKBNK
2021-12-27 10:00:00+03:00 3.2338 YKBNK
2021-12-27 11:00:00+03:00 3.2517 YKBNK
2021-12-27 12:00:00+03:00 3.2517 YKBNK
2021-12-27 13:00:00+03:00 3.2517 YKBNK
... ... ...
2024-01-11 14:00:00+03:00 23.4800 YKBNK
2024-01-11 15:00:00+03:00 23.3200 YKBNK
2024-01-11 16:00:00+03:00 23.3400 YKBNK
2024-01-11 17:00:00+03:00 23.3000 YKBNK
2024-01-11 18:00:00+03:00 23.3000 YKBNK
[5104 rows x 2 columns]
[*********************100%%**********************] 1 of 1 completed
2021-12-27 06:00:00+00:00 3.1711
2021-12-27 07:00:00+00:00 3.2338
2021-12-27 08:00:00+00:00 3.2517
2021-12-27 09:00:00+00:00 3.2517
2021-12-27 10:00:00+00:00 3.2517
2021-12-27 11:00:00+00:00 3.2249
2021-12-27 12:00:00+00:00 3.2697
2021-12-27 13:00:00+00:00 3.2965
2021-12-27 14:00:00+00:00 3.2786
2021-12-27 15:00:00+00:00 3.2786
2021-12-28 06:00:00+00:00 3.2607
2021-12-28 07:00:00+00:00 3.1711
2021-12-28 08:00:00+00:00 3.1532
2021-12-28 09:00:00+00:00 3.1532
2021-12-28 10:00:00+00:00 3.1443
2021-12-28 11:00:00+00:00 3.1532
2021-12-28 12:00:00+00:00 3.1532
2021-12-28 13:00:00+00:00 3.1353
2021-12-28 14:00:00+00:00 3.0816
2021-12-28 15:00:00+00:00 3.0816
2021-12-29 06:00:00+00:00 3.0726
2021-12-29 07:00:00+00:00 3.0547
2021-12-29 08:00:00+00:00 3.0994
2021-12-29 09:00:00+00:00 3.0905
2021-12-29 10:00:00+00:00 3.0816
2021-12-29 11:00:00+00:00 3.0905
2021-12-29 12:00:00+00:00 3.0994
2021-12-29 13:00:00+00:00 3.0994
2021-12-29 14:00:00+00:00 3.0994
2021-12-29 15:00:00+00:00 3.0994
2021-12-30 06:00:00+00:00 3.1263
2021-12-30 07:00:00+00:00 3.1532
2021-12-30 08:00:00+00:00 3.1711
2021-12-30 09:00:00+00:00 3.1711
2021-12-30 10:00:00+00:00 3.1801
2021-12-30 11:00:00+00:00 3.1711
2021-12-30 12:00:00+00:00 3.1711
2021-12-30 13:00:00+00:00 3.1622
2021-12-30 14:00:00+00:00 3.1263
2021-12-30 15:00:00+00:00 3.1174
2021-12-31 06:00:00+00:00 3.0994
2021-12-31 07:00:00+00:00 3.0905
2021-12-31 08:00:00+00:00 3.0994
2021-12-31 09:00:00+00:00 3.1084
2021-12-31 10:00:00+00:00 3.0636
2021-12-31 11:00:00+00:00 3.0636
2021-12-31 12:00:00+00:00 3.0457
2021-12-31 13:00:00+00:00 3.0367
2021-12-31 14:00:00+00:00 3.0189
2021-12-31 15:00:00+00:00 3.0278
Name: price, dtype: float64
2024-01-10 09:30:00+00:00 22.260000
2024-01-10 10:00:00+00:00 22.340000
2024-01-10 10:30:00+00:00 22.480000
2024-01-10 11:00:00+00:00 22.420000
2024-01-10 11:30:00+00:00 22.480000
2024-01-10 12:00:00+00:00 22.360000
2024-01-10 12:30:00+00:00 22.540001
2024-01-10 13:00:00+00:00 23.080000
2024-01-10 13:30:00+00:00 23.320000
2024-01-10 14:00:00+00:00 23.540000
2024-01-10 15:00:00+00:00 23.560000
2024-01-11 06:00:00+00:00 23.680000
2024-01-11 06:30:00+00:00 23.480000
2024-01-11 07:00:00+00:00 23.300000
2024-01-11 07:30:00+00:00 23.420000
2024-01-11 08:00:00+00:00 23.480000
2024-01-11 08:30:00+00:00 23.500000
2024-01-11 09:00:00+00:00 23.560000
2024-01-11 09:30:00+00:00 23.520000
2024-01-11 10:00:00+00:00 23.460000
2024-01-11 10:30:00+00:00 23.520000
2024-01-11 11:00:00+00:00 23.480000
2024-01-11 11:30:00+00:00 23.440001
2024-01-11 12:00:00+00:00 23.320000
2024-01-11 12:30:00+00:00 23.500000
2024-01-11 13:00:00+00:00 23.340000
2024-01-11 13:30:00+00:00 23.340000
2024-01-11 14:00:00+00:00 23.300000
2024-01-11 14:30:00+00:00 23.299999
2024-01-11 15:00:00+00:00 23.300000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00 1.690000e-02
2018-01-02 11:00:00+03:00 1.690000e-02
2018-01-02 12:00:00+03:00 0.000000e+00
2018-01-02 13:00:00+03:00 0.000000e+00
2018-01-02 14:00:00+03:00 1.670000e-02
...
2024-01-11 13:00:00+00:00 -1.600000e-01
2024-01-11 13:30:00+00:00 1.525879e-07
2024-01-11 14:00:00+00:00 -4.000015e-02
2024-01-11 14:30:00+00:00 -7.629395e-07
2024-01-11 15:00:00+00:00 7.629395e-07
Name: price, Length: 19221, dtype: float64
ADF Statistic: -21.08167856949447
p-value: 0.0
Critical Values: {'1%': -3.4306910782595423, '5%': -2.8616907442460104, '10%': -2.5668502371052324}
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
warnings.warn("Maximum Likelihood optimization failed to "
SARIMAX Results
==============================================================================
Dep. Variable: price No. Observations: 19222
Model: ARIMA(30, 1, 1) Log Likelihood 14842.044
Date: Thu, 11 Jan 2024 AIC -29620.088
Time: 22:30:36 BIC -29368.448
Sample: 0 HQIC -29537.601
- 19222
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -0.4303 0.118 -3.640 0.000 -0.662 -0.199
ar.L2 0.2249 0.088 2.557 0.011 0.053 0.397
ar.L3 -0.0466 0.005 -9.454 0.000 -0.056 -0.037
ar.L4 0.0165 0.008 2.072 0.038 0.001 0.032
ar.L5 -0.0389 0.005 -7.184 0.000 -0.050 -0.028
ar.L6 0.0342 0.007 4.703 0.000 0.020 0.048
ar.L7 -0.0216 0.007 -3.177 0.001 -0.035 -0.008
ar.L8 0.0283 0.007 4.094 0.000 0.015 0.042
ar.L9 -0.0334 0.008 -4.408 0.000 -0.048 -0.019
ar.L10 0.0391 0.008 5.184 0.000 0.024 0.054
ar.L11 0.0086 0.007 1.215 0.224 -0.005 0.023
ar.L12 -0.0304 0.007 -4.630 0.000 -0.043 -0.018
ar.L13 -0.0096 0.006 -1.647 0.100 -0.021 0.002
ar.L14 -0.0278 0.005 -5.093 0.000 -0.039 -0.017
ar.L15 0.0008 0.006 0.124 0.901 -0.012 0.013
ar.L16 -0.1492 0.004 -37.889 0.000 -0.157 -0.142
ar.L17 -0.0402 0.018 -2.183 0.029 -0.076 -0.004
ar.L18 0.3794 0.011 35.270 0.000 0.358 0.400
ar.L19 0.2830 0.042 6.801 0.000 0.201 0.365
ar.L20 -0.2598 0.046 -5.591 0.000 -0.351 -0.169
ar.L21 -0.0879 0.017 -5.188 0.000 -0.121 -0.055
ar.L22 0.0040 0.016 0.244 0.807 -0.028 0.036
ar.L23 -0.0482 0.007 -6.829 0.000 -0.062 -0.034
ar.L24 0.0126 0.009 1.362 0.173 -0.006 0.031
ar.L25 -0.0375 0.006 -5.967 0.000 -0.050 -0.025
ar.L26 0.0345 0.008 4.383 0.000 0.019 0.050
ar.L27 -0.0138 0.007 -1.886 0.059 -0.028 0.001
ar.L28 0.0190 0.007 2.766 0.006 0.006 0.032
ar.L29 -0.0649 0.007 -9.387 0.000 -0.078 -0.051
ar.L30 0.0676 0.010 6.740 0.000 0.048 0.087
ma.L1 -0.3107 0.118 -2.628 0.009 -0.542 -0.079
sigma2 0.0125 5.24e-05 238.275 0.000 0.012 0.013
===================================================================================
Ljung-Box (L1) (Q): 0.11 Jarque-Bera (JB): 212572.43
Prob(Q): 0.74 Prob(JB): 0.00
Heteroskedasticity (H): 74.35 Skew: -0.35
Prob(H) (two-sided): 0.00 Kurtosis: 19.28
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19222 23.207419
19223 23.042690
19224 23.130090
19225 23.155577
19226 23.196759
19227 23.198195
19228 23.173311
19229 23.161470
19230 23.178304
19231 23.219559
Name: predicted_mean, dtype: float64
lower price upper price
19222 22.988375 23.426463
19223 22.816413 23.268966
19224 22.843527 23.416653
19225 22.858615 23.452539
19226 22.860389 23.533128
19227 22.851311 23.545079
19228 22.793537 23.553086
19229 22.771669 23.551270
19230 22.758972 23.597636
19231 22.791612 23.647507
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
submissions
{'THYAO': array([246.72844711, 246.78783408, 246.9015646 , 247.04747162,
247.21927253, 247.20683269, 247.09158936, 247.06970646,
246.88399065, 246.69084918]),
'AKBNK': array([41.21498555, 41.00335625, 40.9756319 , 41.09456064, 41.06168266,
41.15711143, 41.0643389 , 41.14454865, 41.06272159, 41.05295957]),
'ARCLK': array([129.63910393, 129.56538056, 129.32843629, 129.40164336,
129.31511734, 129.60642998, 129.43727861, 129.51283904,
129.41582614, 129.59837851]),
'ASELS': array([46.88617574, 46.84543606, 46.85122809, 46.816103 , 46.82289484,
46.78491206, 46.74780343, 46.71490225, 46.66539834, 46.63860407]),
'BIMAS': array([328.78062314, 328.66001175, 328.22562358, 328.25766629,
328.29920129, 328.80807997, 328.01343952, 328.24228676,
328.07770633, 328.29868007]),
'DOHOL': array([11.99364808, 11.9893169 , 11.99364603, 11.98947157, 11.99346285,
11.98965457, 11.99328641, 11.98982327, 11.99312545, 11.98997677]),
'EKGYO': array([8.1760892 , 8.16861706, 8.17628254, 8.16872132, 8.17610686,
8.16891115, 8.17591729, 8.16909689, 8.17573618, 8.16927327]),
'EREGL': array([44.03397781, 43.65898765, 43.5219273 , 43.60978512, 43.59964088,
43.5439619 , 43.45007406, 43.3357611 , 43.28272702, 43.19063575]),
'FROTO': array([779.87342804, 778.04053532, 775.83986295, 777.67485481,
777.12717702, 779.22275821, 778.1245483 , 779.73898544,
777.84203238, 778.29933116]),
'GUBRF': array([141.14868834, 140.66977052, 140.29267173, 140.02522546,
139.92420721, 139.84095072, 139.5790833 , 139.32688028,
139.12621956, 138.88119294]),
'GARAN': array([66.41754158, 66.53585287, 65.82444262, 66.19984193, 65.98487337,
66.03820567, 65.97803918, 65.81657186, 65.92891955, 66.03950152]),
'KRDMD': array([26.00348312, 25.9451383 , 25.94373154, 25.97031833, 26.0845764 ,
26.06383759, 26.11426207, 26.11315538, 26.14346082, 26.21395149]),
'KCHOL': array([153.66963405, 153.73156044, 154.24359365, 154.41723085,
155.15239297, 155.51485854, 155.7242077 , 155.9434715 ,
156.05799333, 156.3638059 ]),
'KOZAL': array([20.18335777, 20.10946599, 20.19596036, 20.14809994, 20.16828438,
20.07120736, 20.12323704, 20.10293381, 20.10383275, 20.11297086]),
'KOZAA': array([43.58409714, 43.63007384, 43.66671951, 43.67640041, 43.68518527,
43.74358314, 43.77852245, 43.85590848, 43.93533953, 43.96617439]),
'PGSUS': array([711.68701056, 711.5365248 , 711.64407539, 711.85089627,
712.53301439, 712.68061064, 712.85942809, 713.40377985,
713.23125806, 713.06654395]),
'PETKM': array([20.01677177, 19.99448174, 19.97336809, 19.94793367, 19.92779044,
19.91660949, 19.90646131, 19.90145363, 19.88604725, 19.85763811]),
'SAHOL': array([68.38458445, 68.2174538 , 68.18155277, 68.29138276, 68.47217024,
68.47658219, 68.27603463, 68.22292766, 68.24880712, 68.40455454]),
'SASA': array([35.07289163, 35.00749169, 34.98824863, 34.96465225, 34.9545911 ,
34.96251762, 34.95880461, 34.95779422, 34.95283677, 34.94501868]),
'SISE': array([47.75245292, 47.77226825, 47.71063486, 47.75187081, 47.74509235,
47.80246512, 47.7562865 , 47.76060881, 47.74026177, 47.75606647]),
'TAVHL': array([117.84888577, 117.88400361, 117.86808255, 117.89298502,
117.90717193, 117.95666838, 117.96566047, 118.0220838 ,
118.08621704, 118.02767048]),
'TKFEN': array([37.57401907, 37.71791395, 37.57580652, 37.58368395, 37.58521004,
37.57349133, 37.60297795, 37.52268025, 37.48154465, 37.56920221]),
'TUPRS': array([140.17831703, 140.33654821, 140.13103461, 139.50768859,
139.84930153, 140.15384346, 139.52881033, 139.4866555 ,
139.48659775, 139.64620324]),
'TTKOM': array([26.76106245, 26.6578966 , 26.67664835, 26.53237998, 26.56686002,
26.50274109, 26.448061 , 26.37888021, 26.3160813 , 26.28693917]),
'TCELL': array([60.84638811, 60.76452249, 60.82680552, 60.79867889, 60.8232561 ,
60.85986885, 60.80905317, 60.83287196, 60.7649962 , 60.87972188]),
'HALKB': array([13.02436095, 13.02755185, 13.02814561, 13.01921732, 13.01801652,
13.0209602 , 13.02302678, 13.02065749, 13.01620852, 13.01810905]),
'ISCTR': array([25.76553266, 25.57376328, 25.61390867, 25.64279202, 25.66735909,
25.6504995 , 25.67051812, 25.70289366, 25.73571323, 25.72625894]),
'VAKBN': array([14.60094897, 14.60895631, 14.60258455, 14.59147041, 14.59253866,
14.59224751, 14.59837002, 14.59702767, 14.59077751, 14.58943921]),
'VESTL': array([48.36190757, 48.34774533, 48.32830684, 48.32497134, 48.31210428,
48.31125819, 48.31166112, 48.32768156, 48.32840434, 48.32827484]),
'YKBNK': array([23.20741887, 23.04268983, 23.13008961, 23.15557701, 23.19675869,
23.198195 , 23.17331142, 23.16146988, 23.17830398, 23.21955939])}
import numpy as np
submission = {key: value.tolist() for key, value in submissions.items()}
print(submission)
{'THYAO': [246.72844710723302, 246.78783407728295, 246.9015646038812, 247.04747162124343, 247.21927253115817, 247.206832691938, 247.09158936183306, 247.06970645523555, 246.8839906543494, 246.69084918318993], 'AKBNK': [41.21498555020162, 41.003356245551444, 40.97563189982208, 41.09456064284728, 41.061682660223795, 41.15711143390959, 41.06433890285226, 41.14454865391768, 41.062721588704555, 41.05295956678886], 'ARCLK': [129.63910392729005, 129.5653805588673, 129.32843629297253, 129.4016433612058, 129.31511733975594, 129.6064299831944, 129.43727860615024, 129.51283903684234, 129.4158261358366, 129.59837851217276], 'ASELS': [46.88617574472465, 46.84543605887873, 46.85122808676905, 46.816103004331715, 46.822894838354046, 46.7849120619001, 46.74780343144717, 46.714902254691836, 46.665398341571674, 46.63860407377028], 'BIMAS': [328.7806231443262, 328.660011754543, 328.22562358258256, 328.257666293802, 328.2992012866527, 328.80807996561924, 328.01343952038064, 328.2422867609498, 328.0777063340028, 328.29868006578994], 'DOHOL': [11.993648080732326, 11.989316897335875, 11.993646028255652, 11.98947156928321, 11.993462853579727, 11.989654570889067, 11.993286409521632, 11.989823268161206, 11.993125448107174, 11.989976771724155], 'EKGYO': [8.176089198026078, 8.168617058890318, 8.17628254176901, 8.16872131584169, 8.176106860757438, 8.168911151105908, 8.175917294484885, 8.169096891952474, 8.175736181456779, 8.169273270781389], 'EREGL': [44.033977808646775, 43.65898764706641, 43.52192729761015, 43.60978512412417, 43.59964088318589, 43.54396190254043, 43.450074056645256, 43.335761103158546, 43.282727016516255, 43.19063575180934], 'FROTO': [779.8734280437163, 778.040535324543, 775.8398629549137, 777.6748548062105, 777.1271770156923, 779.2227582076428, 778.1245482998045, 779.7389854447762, 777.8420323760793, 778.2993311606198], 'GUBRF': [141.14868833606002, 140.66977052373915, 140.2926717268744, 140.02522545858636, 139.9242072059186, 139.84095071897008, 139.57908329880217, 139.3268802803794, 139.1262195621227, 138.8811929435401], 'GARAN': [66.41754157833445, 66.53585286787546, 65.82444262019675, 66.19984193302288, 65.98487336967912, 66.03820566668699, 65.97803918002698, 65.81657186437555, 65.92891954947265, 66.03950152208415], 'KRDMD': [26.003483119174458, 25.945138297880394, 25.943731541433944, 25.97031833106211, 26.084576403334086, 26.063837593446028, 26.11426207202655, 26.11315538255608, 26.14346082223534, 26.21395149198643], 'KCHOL': [153.66963405401702, 153.7315604371095, 154.24359364903768, 154.41723084727934, 155.15239296621877, 155.51485854332546, 155.72420770380123, 155.9434715033698, 156.0579933349858, 156.36380589821755], 'KOZAL': [20.183357772837123, 20.10946598605728, 20.19596035657695, 20.148099942729207, 20.168284379075654, 20.07120735780075, 20.123237037077896, 20.10293381350078, 20.103832746979915, 20.112970859929025], 'KOZAA': [43.58409713509102, 43.63007384034846, 43.66671951126056, 43.67640040639069, 43.68518527393528, 43.74358313974144, 43.77852245417585, 43.85590848162289, 43.93533952847479, 43.96617438567008], 'PGSUS': [711.6870105562964, 711.536524799276, 711.6440753868644, 711.8508962692003, 712.5330143946644, 712.6806106413915, 712.8594280906352, 713.4037798471778, 713.2312580640764, 713.0665439470387], 'PETKM': [20.01677176893671, 19.994481740056173, 19.973368088184127, 19.947933665007817, 19.927790442035466, 19.91660948655672, 19.906461309311435, 19.901453628973528, 19.886047250272114, 19.85763811401552], 'SAHOL': [68.3845844480737, 68.21745379648303, 68.18155277127364, 68.29138276136075, 68.47217024429902, 68.4765821858676, 68.2760346251081, 68.22292766205862, 68.24880712057283, 68.40455454160326], 'SASA': [35.07289162969731, 35.0074916936396, 34.98824863216797, 34.96465224704032, 34.95459110419446, 34.96251761524504, 34.958804608720634, 34.9577942176785, 34.95283676890159, 34.945018677702876], 'SISE': [47.75245292108193, 47.77226824615632, 47.71063485953328, 47.75187080968883, 47.74509234582463, 47.8024651212386, 47.75628650037016, 47.76060881261082, 47.74026176660561, 47.75606647443745], 'TAVHL': [117.84888576546061, 117.88400360910454, 117.86808255475137, 117.8929850188489, 117.9071719277577, 117.95666838100395, 117.96566046535648, 118.02208379861436, 118.08621704416655, 118.02767048197585], 'TKFEN': [37.574019071922955, 37.71791395434733, 37.57580651720906, 37.583683953908455, 37.58521003562684, 37.573491327158486, 37.60297794877059, 37.52268024961215, 37.4815446545906, 37.569202208787225], 'TUPRS': [140.17831702571735, 140.33654821068933, 140.13103461456555, 139.5076885946764, 139.8493015287174, 140.15384345909465, 139.52881032602934, 139.48665549888972, 139.4865977452933, 139.64620323797956], 'TTKOM': [26.761062454570094, 26.6578965954013, 26.676648346093806, 26.5323799770588, 26.566860015557413, 26.502741090922882, 26.448060997741788, 26.37888020763176, 26.316081299524747, 26.28693916999035], 'TCELL': [60.846388107561275, 60.76452248634553, 60.82680551564222, 60.798678891385684, 60.82325609915799, 60.85986885423484, 60.809053169830264, 60.832871955881096, 60.764996201238155, 60.8797218816646], 'HALKB': [13.024360946385308, 13.027551854513327, 13.02814560651817, 13.019217323616965, 13.018016524704374, 13.020960203255427, 13.023026781385669, 13.020657489793118, 13.016208518193404, 13.018109052410637], 'ISCTR': [25.765532658963668, 25.57376328405084, 25.613908668205458, 25.642792023906694, 25.66735909487472, 25.650499499627152, 25.67051811776372, 25.70289365577941, 25.735713232556755, 25.726258942402637], 'VAKBN': [14.600948968404444, 14.608956306749047, 14.602584554909672, 14.591470406399264, 14.592538661014462, 14.592247509005302, 14.598370018737963, 14.59702766599865, 14.590777511610774, 14.589439209184151], 'VESTL': [48.36190757277486, 48.347745325123654, 48.328306841442604, 48.32497134030203, 48.31210428274845, 48.31125818597144, 48.3116611183435, 48.32768156441343, 48.32840433503494, 48.32827483820829], 'YKBNK': [23.207418865505897, 23.04268982835163, 23.130089607032154, 23.15557701265702, 23.196758689846163, 23.198194995599696, 23.17331142323614, 23.161469881125882, 23.17830398151349, 23.21955938808481]}
len(submission)
30
length=0
for pred in submission.values():
length += len(pred)
length
300
length=0
for pred in submission.values():
print(len(pred))
10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10